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June, 16 2025

3 Ways AI is Conquering Uncertainty in Chemicals

By Alex Sorokin, Business Consulting Engineer, Imubit Across dozens of speakers at the 2025 World Petrochemical Conference (WPC), a key theme was repeated – the chemicals industry faces a period of uncertainty driven by geopolitics, trade conflicts, and supply chain volatility. However, plant operators cannot control those external factors. They must focus on what they can control: embracing innovation, driving operational excellence, and maximizing margins on existing assets. At WPC, industry leaders and analysts often pointed to AI as the enabling technology for achieving these goals. However, a critical question was left unanswered: how can AI help chemical plants become more resistant to uncertainty? AI is a Journey, Not a Short Cut Perhaps it’s not surprising that WPC did not offer a direct, unified answer – AI is not a single-point solution, but rather a set of tools, applications, and approaches. The possibilities are uncountable, and the enterprise transformation potential has not been matched by anything since Microsoft Excel was introduced a generation ago. But that also makes it hard to know where to start, how to show value from AI-enabled projects, and how to continue capturing this value persistently after a few weeks or months. One can see why as few as 1 in 10 digital transformation efforts succeed in traditional industries [1] – but the results are worthwhile, with early adopters outperforming peers by a factor of 2-3x [2, 3]. Learning Together With, and From, Our Customers At Imubit, we’ve observed that the most successful AI initiatives are those which empower cross-functional teams to immediately understand the benefits of new technology first-hand. Our customers, and the challenges they’ve faced with other solutions, have helped us ensure that the Imubit Industrial AI Platform does just that. One Chevron refinery used the platform to drive “conversations between multiple stakeholders that hadn’t been able to be done before, namely the planning group, operations and engineering,” accelerating the site’s planning cycle from weekly to real-time. At multiple Oxbow calcining sites, the platform has allowed operators, engineers, and leadership to “see immediate improvements [and] provided the basis … to expand into other improvements that [they] didn’t even know were possible.” By partnering with these customers and dozens of others in dynamic, elastic markets, we have developed industry-leading expertise in separating signal from noise and deriving concrete results from high-level goals – even as operations, economics, and the goals themselves evolve over time. As with any work process, those experiences have led to learnings and those learnings have been distilled into best practices. To tackle uncertainty, remain competitive, and succeed on the AI journey, follow these 3 proven best practices: 1. Break Out of Local, Single-Point Optimization Operating points across different areas must shift to meet common, system-wide goals. Drive to the optimal operating points plant-wide by using the right feeds, catalysts, and process conditions within process and environmental limits. Systemwide automation has been especially challenging to implement with traditional methods due to the number of constraints, scenarios, and people involved. An optimization solution must understand how interactions between different process units affect high-level, plant-wide goals and constraints. For example, if one process unit shuts down or has an unscheduled catalyst change, how should other units be operated to re-optimize the plant? In one such example, Imubit’s AI Optimization (AIO) solution dynamically adjusts multiple units’ feed rates and APC targets to compensate for run-limiting equipment fouling – and then automatically readjusts when equipment is cleaned. Elsewhere, our AIO models have challenged long-established process unit control strategies to unlock value plant-wide. For more details on this example, check out case study #2 in this Hydrocarbon Engineering article. The lesson learned is consistent across multiple customers, geographies, and industries: true value chain optimization requires greater coordination and flexibility than is possible with a series of disconnected single-point solutions. 2. Maximize Existing Assets to Their True Constraints Reduce giveaway, downtime, and suboptimal uptime by running to true underlying limits even as feeds vary, equipment fouls, and catalysts degrade. At the low points of the industry’s market cycles – such as the extended downturn ongoing since 2022 [4] – plants are simultaneously the most challenged to turn a profit and the most strapped for capital to unlock margins through equipment upgrades. This means that existing assets must be maximized further than ever before. But even the best human operators often run to conservative targets to avoid deactivating catalysts, degrading equipment, or violating poorly-understood limits. While existing automations can reduce the throughput or margin left on the table, significant value is left unrealized when the true underlying limits are poorly understood, nonlinear, or subject to long time dynamics. These challenges are a natural fit for AI. In several implementations on semi-batch delayed-coking units, Imubit’s AI solution has dynamically maximized the value of each batch, reducing sub-optimal asset utilization by over 20%. For a customer with two parallel mixed-feed steamcrackers, Imubit’s AI Optimizer is able to capture a 3% margin increase by adjusting feed rates to true coking limits and downstream constraints instead of historically assumed values. In these examples and in countless others, businesses that will survive and thrive tomorrow must use novel solutions such as AIO to make the most of the equipment they have today. 3. Make Technology a Force Multiplier Enhance the functional understanding and decision-making of process experts with no-code modeling capabilities and a suite of AI explainability tools. Organizational adoption is a major bottleneck to capturing value from automation and digitalization initiatives. This has been true for 20th-century technologies like APC – in some cases, less than 10% of APCs are tuned and maintained [5] – and businesses rightly fear that this will apply to 21st-century technologies, too. But one truly revolutionary aspect of AI is its accessibility across disciplines, which provides the ability to empower the entire workforce and democratize expertise. Imubit’s platform is designed with an intuitive point-and-click user interface to lower the barrier to entry of AI. With an hour of training, users with no AI expertise can build their own models, review AI insights, and instantly compare what-if scenarios. Some facilities integrate Imubit models into operator training programs as a safe environment to test moves and duel the AI optimizer. Others use model learnings to augment established workflows, from scheduling APC gains to updating LP vectors. Today, industry is more reliant on technology than ever before, but that technology needs willing and capable users. To capture sustained value in this environment, companies must invest in the right AI solutions – those designed from the ground up to be explainable and easy to use. AIO is shaking up the way companies think about process optimization and delivering the agility required to maximize profitability in times of uncertainty. For more on AIO and how it can transform your operations, visit imubit.com/aio.
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June, 16 2025

Answering the Important Questions

Reinforcement Learning? Industrial AI? Closed Loop AI Optimization? Imubit webinars spark thought provoking questions from industry professionals like you. By Allison Buenemann, Product Marketing Manager at Imubit Audience participation was at an all-time high in our most recent webinar featuring Imubit customer Big West Oil. With a record number of audience questions raised, we decided to make the answers more accessible by assembling our most frequently asked questions into this blog post. Product Architecture FAQs Application / Case Study FAQs Continuous Improvement FAQs Technology Comparison FAQs People FAQs Product & Architecture FAQs Q: Can you elaborate on Imubit’s architecture and how the software is able to share data with everyone? A: Imubit’s architecture consists of two main components 1) the cloud-based Industrial AI platform (which provides a collaborative environment for members of all teams to build, evaluate, and track performance of the closed loop models), and 2) the on-premises closed loop Deep Learning Process Control application. There are two on-premises deployment options for DLPC in the DMZ and PCN. There are no two-way exchanges of data between the on-premises controller and the cloud database. The on-premises closed loop optimization communicates with numerous on-prem sources, including the DCS, laboratory, and data historians, utilizing OPC/OPC-UA an industry standard protocol. Q: Is the runtime control running on the process control network (PCN)? A: Imubit’s on-prem DLPC application can connect to either the DCS or to existing APC. The software sits on the PCN level in order to communicate with these systems. Data extraction for model training from the site historian is typically done from the business network. Q: How does Imubit integrate with other systems to get economic, pricing, or cost data for the optimization? A: Some plants will push prices from their LP to their historian or straight to the DCS. When this is the case, Imubit can see in real time when there are changes to the model based on updates to the price deck. Some customers prefer to have more control over when these updates occur and in these cases, they will manually enter price changes into the on-prem DLPC application. In order for models to effectively control/optimize need up to date prices… So the more often you can input changes to prices, the better a job Imubit can do at optimizing! Q: How often do you collect data from the plant and how often do you update set points? A: This is a configurable parameter, but typically data is collected every 3 to 5 minutes. The range for all Imubit applications spans from a 1 minute read/write frequency to up to an hour. Q: Does Imubit DLPC have automated feedback (lab results, analyzer results) to help adjust inferentials without human intervention so the model stays “up to date” with slow changes to unit operation or feedstock quality? A: Imubit biases inferentials onsite using cumulative sum method or straight linear biasing method. This is something that the Implementation Engineering team works with the onsite team to do. This is very similar to how other technologies bias their inferrentials to prioritize most recent lab results.   Application FAQs Q: Has Imubit worked with other refineries to solve similar optimization problems [around FCC units]? A: Imubit has >70 live DLPC applications running within our global customer base (as of this 2Q2024 publish date), and more than 20 of these are on FCC units. These applications span optimization strategies on all stages of FCC operation including preheat, fractionation, reactor/regen, C3/C4 splitter, gas plant, and others. Q: Can you provide more details on the application of Imubit DLPC for FCC units? A: Imubit has extensive experience working on optimization strategies for FCCs as highlighted above. For a full example, download our FCC case study. Q: Has Imubit implemented DLPC in ethylene plants? A: Imubit has experience working on optimization strategies for C2 and C3 splitters and steamcracker furnaces, and we’re always looking to explore new ways to apply the technology! If you have an idea for how Imubit’s closed loop AI could help optimize your unit we would love to schedule an introductory demo call to hear about it. Q: Can you elaborate on how you would handle solving multi-objective optimization problems? A: Imubit models can be trained using multiple objectives. In training, tunable parameters are introduced, so that in real time, the user can update the relative importance of each objective. Q: For FCCs with varied feed composition, does the DLPC need to take that into account? A: Yes, the model definitely needs to, and does, take these different operating modes into account. For example, Imubit customer Big West Oil mentioned having 3-4 base crudes in varying percentages. The key is to ensure your training data set contains enough operating data for each of the different feed composition regimes for the reinforcement learning-trained controller to know how to respond optimally to the hundreds of millions of different potential scenarios that could be encountered during real time operations.   Continuous Improvement FAQs Q: Do you have any examples of how your application is able to work around instrument limitations (for example, minimally instrumented sites, or an MV or CV outage)? A: If it’s unplanned, the model will turn itself off. There can be contingencies built into the model for planned MV outages, for example an instrument on a line that’s only run through a certain percentage of the time. These are called flexible or flex MVs. With CVs, can have flexible constraints if you are only controlling to a constraint some of the time. Some other features that you can use include bad value protection along with instructions of what to do in case of values in that range. Q: How much input does a DPLC need from a process or process control engineer? Are the Deep Learning models manipulable? If yes, at which degree? A: Process and Process Control Engineers are brought into the modeling process to incorporate their domain expertise. The deep learning models aren’t magic, and we can’t just throw data at them. We need the domain experts to define the “rules of the game” – i.e. which variables are independent / dependent, the economic objective, the time to steady-state, constraints, etc. We let the model learn as much as possible from the data, but when important information isn’t perfectly contained within the data, then we leverage the knowledge of your experts to incorporate information like mass balance or gain tuning. We use Automated Machine Learning (AutoML), to translate this domain expertise into data science parameters. Q: How long would it take to retrain the DLPC model on a new operating regime? For example, in the webinar with Big West where their crude unit was updated during a turnaround? A: The rate limiting step in retraining a DLPC model on a new operating regime is collecting enough data in that regime to represent a broad spectrum of operations. This typically means collecting a minimum of a couple of months worth of operating data. Once the data is collected the time for the retrains to be executed is typically two weeks or less. Q: How often must the model(s) be retrained, and which plant personnel are involved (Process engineers, process control/DCS engineers, economics and planning, etc.)? A: Beyond the initial model training, subsequent retrains really do not require too much input from the plant. Because Imubit is collecting plant data continuously, there is no burden on the customer/plant to export data. As part of the continuous improvement workflow, the Imubit team is continuously monitoring customer’s DLPC applications, and being proactive about suggesting retrains when they see shifts in operation. The Imubit team is able to do the majority of the model retrain process without requiring customer assistance, though we like to bring in customers to review updates to the model following a retrain.   Technology Comparison FAQs Q: What are the advantages of using Imubit DLPC over any other traditional APC package? A: While there are a lot of technical differences between the way Imubit DLPC works compared to traditional APC, a couple of highlights include 1) the way that the dynamic process simulator is trained using a neural network (rather than first principles), and 2) the use of Reinforcement Learning (RL) to train and pre-solve for all possible optimization solutions in an offline cloud environment (no online solver). Training the process simulator with a data-first approach frees us of the limiting linearizations, approximations, and assumptions of first principles-based models. The use of RL to do offline solving of all possible scenarios is unique to Imubit. RL is an incredibly strong and powerful technology because of the scale of the training that is capable. It quite literally learns similar to how a human brain learns. For example, if you’re learning to walk as a child, you learn through trial and error. The APC analogy of walking is that every step you take you get out your protractor/ruler/calculator, measure the gravitational force and you calculate in real-time, how high you need to lift your foot to take a step. From Big West Oil – After evaluating APC many times it felt like it would be resource intensive and we might be limited in the results delivered by our limited instrumentation. Imubit felt like the next innovation in the closed loop optimization and control world. It was something that would work even with our limited instrumentation, and where a lot of the supporting work to implement and maintain would be done by the Imubit team.   People FAQs Q: [to Imubit customer] How difficult has it been to sell operations teams at Big West Oil on AI-based process control? A: At first, operations were skeptical. They were skeptical of 1) whether the technology would work, and 2) if it would put them out of a job. Starting with a simple model for DLPC helped a lot in building operator trust. By focusing on the debutanizer tower, which had only two MV handles which they were familiar with and knew how to interpret, they were able to see the results and understand them. Building this initial trust is critical to moving on to more complex control paradigms where explainability gets trickier.
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October, 06 2025

Step-by-Step to a Self-Optimizing Petrochemical Plant with AI

Process industry leaders have witnessed remarkable benefits after implementing advanced control projects, with operational efficiency gains reshaping bottom lines across facilities worldwide. Yet capturing that value has never been harder. Volatile margins, tightening sustainability mandates, and complex global regulations now collide with aging assets and fragmented data systems. Rising fuel prices amplify every inefficiency, while decarbonization targets demand transparent, data-backed progress. This roadmap turns those constraints into a self-optimizing advantage. Each stage builds technical capability and operator confidence, pairing modern instrumentation with a culture that values continuous improvement. The result isn’t just smarter software—it’s an organization ready to learn and adapt in real-time. Build a Solid Data & Instrumentation Foundation Every optimization project lives or dies on data quality. You need clean, time-synchronized signals streaming from the field into your plant data system before any advanced analytics can deliver value. Modern digital field instruments capture temperature, pressure, flow, level, and composition with high precision and fast sampling rates, giving algorithms the stable footing they require. Start by confirming a few non-negotiables: calibrated sensors, data-server uptime, a closed lab quality loop, and a single clock governing both operations and IT. Ownership matters too; someone must guard governance, calibration frequency, and tag naming standards.  Core instruments—thermocouples, differential-pressure transmitters, magnetic flow meters, radar level gauges, and online chromatographs—are generally expected to provide high measurement accuracy and reliability. Fragmented legacy networks, drifting analyzers, or missing timestamps create the classic garbage-in/garbage-out trap. By tracking data latency, calibration intervals, and the percentage of healthy tags each week, you establish an objective baseline and a springboard for every step that follows. Strengthen Basic Automation & Control Loops Building on your data foundation, controlling variability creates the stable platform needed for advanced optimization. When control loops drift, energy spikes, and models chase noise rather than opportunity. To sharpen performance, identify struggling controllers through oscillation metrics, run safe step tests to reveal true process dynamics, and apply fresh tuning for immediate stability. Document operating limits in the distributed control system (DCS) and regularly audit loops after changes to maintain gains. As stability improves, your focus shifts from reactive to predictive analysis, with inferentials spotting patterns that manual trending misses. Avoid common pitfalls like “set-and-forget” mindsets and limited operator coaching. Prioritize resources on economically impactful loops—high-pressure compressors, fired heaters, and quality-critical analyzers. Throughout this transition, conservative safety margins and clear operator override capabilities ensure well-documented loops provide advanced process control with its best foundation, transforming routine monitoring into profit-driven action. Apply Advanced Process Control to Key Units With stable control loops in place, now tackle multivariable complexity that basic control can’t handle. Model predictive control (MPC) forecasts unit behavior and adjusts multiple variables simultaneously, balancing profit maximization with safety and quality constraints. Focus on units where economics, disturbances, and data quality align—reactors with strict specifications, energy-intensive columns, or fuel-driven heaters offer immediate margin improvements. Implementation follows a structured approach: gather plant data, build dynamic models, configure controlled variables, embed constraints, test in simulation, then transition to closed-loop operation.  Success requires right-sized models, regular coefficient updates, and early operator involvement. Monitor KPIs like giveaway reduction and energy efficiency, while guarding against model drift through systematic audits. This foundation of stabilized variability and encoded expertise creates the springboard for closed-loop AI. The plant data, established models, and—most critically—operator trust developed in the APC phase enable the evolution from advisory AI to autonomous control, where algorithms make real-time decisions that continuously improve process performance. Introduce AI in Advisory Mode The transition from traditional APC to full automation benefits from an intermediate step that builds confidence while managing risk. Advisory AI sits between conventional control and full autonomy: its models study historical plant data, predict optimal setpoints, and present recommendations that still rely on operator approval. Clean data from calibrated sensors, a clear economic objective, and visible operator engagement form the essential starting point. Once those pieces are in place, data is aggregated, models are trained and validated offline, results appear on intuitive dashboards, and every accepted or rejected suggestion feeds the next learning cycle. When recommendations drift outside the plant’s historical operating window, quick checks on data range coverage, constraint mapping, or the economics feed often expose the root cause. Keeping operators in the loop not only safeguards production; their feedback helps the AI refine inferentials and strengthen trust. Resistance typically stems from opaque algorithms or workflow disruption. Clear explanations of key driver variables, plus side-by-side displays of expected profit impact, can speed adoption and build confidence. In this mode, plants gain a low-risk proving ground where AI learns live behavior, setting the stage for the closed-loop optimization phase that follows. This advisory approach allows teams to validate AI recommendations against their operational expertise while building the foundation for more autonomous optimization strategies. Implement Closed-Loop AI Optimization at Scale Once you trust the guidance coming from advisory models, the next leap is letting those models write setpoints straight into the distributed control system (DCS). Moving to AI-assisted real-time recommendations compresses decision cycles and unlocks incremental margin, a pattern documented in recent deployments of industrial AI. Essential safeguards protect operations during this transition. Rigorous change management processes ensure every modification follows proven protocols, while layered cybersecurity protocols defended by the OT team protect against external threats. Physical manual-override switches in the control room provide immediate operator control when needed, and non-negotiable safety and environmental limits create unbreachable boundaries. Start with one high-value unit: automate control, verify results, mirror the approach on similar assets, then connect adjacent systems. This scaling approach builds confidence while minimizing risk.  Track progress through a focused KPI suite: real-time profit delta, energy per tonne, CO₂ intensity, and quality consistency. Continuous validation routines keep the models accurate, while clear governance reassures teams that closed-loop AI enhances rather than replaces their expertise. Institutionalize an Optimization Culture Technology alone cannot sustain long-term optimization gains—lasting self-optimization demands more than clever algorithms. It thrives on a culture that values learning and collaboration. Start by forming a cross-functional council that sets clear economic targets, schedules monthly performance reviews, and openly celebrates every improvement. Visible leadership sponsorship keeps momentum alive through the inevitable challenges. To win buy-in across operations, engineering, and management, outline a simple readiness checklist: executive commitment, named data owners, structured improvement routines, and baseline digital skills. Share this scorecard widely so each team can see where it stands and track progress. When resistance surfaces, pair transparent communication with quick, low-risk pilots that prove value fast. As AI handles routine tuning, operators gain time for higher-value analysis, while captured know-how feeds future models. Embedding this feedback loop can link daily actions to long-term performance, turning optimization into a habit rather than a project. Your Next Step Toward a Self-Optimizing Petrochemical Plant Six linked steps form a sequential roadmap to a self-optimizing plant. Escalating energy prices and tightening carbon policies make speed critical. Delaying digital maturity exposes you to rising costs and shrinking margins. Assess your readiness, choose a high-value use case, and evaluate AI platforms such as the Imubit Industrial AI Platform to accelerate progress. Early adopters can capture significant production rate increases and energy savings, proven in operations using advanced control and Closed Loop AI Optimization. Imagine front-line operations where AI models learn in real-time, energy intensity drops, and teams focus on strategic decisions instead of manual tuning. For oil and gas industry leaders seeking sustainable efficiency improvements, Imubit’s Closed Loop AI Optimization solution offers a data-first approach grounded in real-world operations. Get a Complimentary Plant AIO Assessment and start today.
Article
October, 06 2025

Step-by-Step to a Self-Optimizing Gas Processing Plant with AI

Surging demand for cleaner energy forces gas processors to extract maximum value from every unit of fuel while controlling costs. Something as basic as an uncalibrated meter can erode margins. A structured journey from reliable data collection to autonomous optimization helps you curb these losses and unlock the next tier of performance.  Plants that complete the full transformation can see significant operational and financial benefits, including $0.25/bbl margin improvement from distillate system optimization and 15-30% reduced natural gas consumption that drops straight to the bottom line. The six-phase roadmap transforms today’s reactive facility into a self-optimizing plant that learns and improves in real time. Each phase builds new capability onto the last, creating a comprehensive transformation that moves beyond traditional operational methods. Build a Solid Data & Instrumentation Foundation Every optimization project rises or falls on data quality. Poor measurement accuracy creates blind spots that generate recurring losses from inaccurate readings across the industry, affecting everything from product value to operational safety. Establishing measurement standards that support reliable decision-making begins with sensors, chromatographs, and analyzers that meet tight accuracy requirements, with timestamps synchronized across systems. Routine calibration through structured scheduling addresses drift before it impacts margins, while comprehensive tag audits push data availability beyond industry standards. Common issues like uncalibrated water-vapor probes can distort hydrate predictions, creating operational risks that proper instrumentation practices help prevent. Robust data platforms that validate, flag, and reconcile multiple inputs keep plant information clean and ready for advanced analytics. This foundation becomes the bedrock for every optimization step that follows, from basic process control improvements to full AI-driven operations. Without a reliable data infrastructure, even the most sophisticated optimization technologies cannot deliver their promised returns, making this initial investment critical to long-term success. Strengthen Automation & Tighten Control Loops Once your sensors are delivering trustworthy numbers, the next lever involves letting controllers, rather than operators, handle routine moves. Poor measurements force loops to chase phantom deviations, wasting energy and eroding margins through systematic inefficiencies. Auditing loop performance with integral absolute error (IAE) or integral squared error (ISE) metrics reveals improvement opportunities. High scores often trace back to valve stiction; cleaning or reseating the actuator before retuning gains can restore responsiveness. Proportional-integral-derivative (PID) settings that damp oscillations within a reasonable timeframe help stabilize operations, with adjustments locked through your distributed control system (DCS). Early automation wins include letting the DCS manage compressor antisurge valves and fine-tuning contactor temperature trims. Conservative gains work best while trending suction temperature and pressure on the HMI helps operators see trouble approaching. Well-behaved loops stabilize flows and temperatures, creating the stable operational environment that advanced process control requires to capture the next level of efficiency gains. Deploy Advanced Process Control (APC) on Key Units Advanced process control creates a predictive layer on top of your distributed control system, adjusting dozens of variables simultaneously to keep units running at their economic sweet spots. Real-time simulations spot when temperatures or pressures drift toward hydrate or phase-envelope limits, nudging setpoints before alarms ever sound. Success starts with disciplined variable screening, dynamic modeling, and focused move tests. Once your model accurately tracks plant behavior, you can commission it and measure profit improvements every shift. Critical-curve visualizations help reconcile any model-plant mismatches that surface during feed swings, keeping operator confidence high. Plants deploying APC across LPG extraction, fractionation, and condensate stabilization achieve tighter product specifications and lower energy consumption. Phased deployment, cross-functional training, and continuous model monitoring help lock in these benefits over time, preparing your operation for the next phase of intelligent optimization. Introduce AI in Advisory Mode Think of advisory-mode AI as a virtual process engineer that studies every tag in real-time, yet leaves final moves to you. It starts by harvesting historian data, then training reinforcement learning models that function like a digital twin.  Once deployed, a web dashboard streams profit deltas alongside variable-importance bars and what-if sliders, letting you see exactly how each recommendation earns or saves money. With AI constantly unifying sensor data, it spots subtle inefficiencies, such as excessive methanol injection in acid-gas dehydration, and suggests lower injection rates that trim reagent spend without risking hydrate formation. Scheduled monthly retraining guards against data drift, while clear audit trails help operators override anything that looks unsafe. The biggest hurdle remains data quality; gaps or stale tags can erode model confidence. Address this early by pairing AI analysts with control-room staff, and you’ll build the trust needed for wider adoption. This advisory phase builds operator confidence and demonstrates value before transitioning to fully autonomous optimization modes. Transition to Closed-Loop AI Optimization Moving from advisory insights to closed-loop action means letting the AIO solution write optimized setpoints directly to the distributed control system.  Plants that reach this stage often see substantial energy cost reductions as the AIO solution continuously shifts operating modes with market swings, results highlighted in studies on operational excellence. Key change-management steps include certifying operators on the new workflow, running side-by-side trials during peak and low-load periods, and logging every AIO decision for audit. Indicators you’re ready for fully closed-loop operation include operators accepting recommendations automatically, stable model accuracy after feed changes, and advisory wins translating into tangible margin lift. Challenges like data latency, cybersecurity concerns, or cultural resistance can be mitigated through redundant sensors, strict network segmentation, and frequent feedback sessions that demonstrate how the AIO solution prevents off-specification incidents and reduces downtime. This autonomous capability sets the foundation for enterprise-wide optimization expansion. Expand & Institutionalize the Optimization Culture Once closed-loop optimization demonstrates value on a single unit, expanding it to refrigeration compression and fractionation systems multiplies results. A coordinated approach allows every unit to balance energy, throughput, and safety considerations in real-time, creating synergies that individual unit optimization cannot achieve. Scaling requires structured governance. Regular cross-functional reviews help teams track key metrics like energy savings, yield improvements, and emissions reductions. Studies on efficiency optimization confirm that disciplined oversight sustains long-term performance gains across multiple operational areas. Technology implementation alone won’t drive lasting change. Simulation-based training helps operators understand AI recommendations before implementing them, while accessible analytics tools encourage exploration and learning. Comprehensive training programs, combined with clear documentation, accelerate adoption across different shifts and teams. Culture becomes the foundation for sustained improvement. When interdisciplinary teams regularly share operational insights, maintenance observations, and financial metrics during routine meetings, data-driven decision-making becomes standard practice. This collaborative approach keeps continuous improvement as a central focus rather than an occasional initiative, ensuring that optimization becomes embedded in daily operations rather than remaining a specialized project. Optimize Your Gas Processing Plant with Imubit’s Closed Loop AI  Return on investment transforms optimization from a project into a core strategy. The formula remains straightforward: (Energy saved + yield gain + emissions credit) – implementation cost. Industry experience confirms the value proposition: Advanced process control delivers substantial commercial benefits, while Closed-loop AI optimization pushes further, learning in real time and writing setpoints directly to your distributed control system. Manage risk through phased deployment: start with one high-impact unit, validate results, then expand systematically. Maintain vigilance over data quality, model retraining, and economic assumptions as conditions change. For process industry leaders seeking sustainable efficiency gains in gas processing, Imubit’s Closed Loop AI Optimization solution offers a data-first approach grounded in operational reality. Kickstart your AI journey with a no-cost assessment and discover how Imubit’s technology can transform your facility’s performance while delivering measurable bottom-line results.
Article
October, 06 2025

How AI Optimization Can Protect Margins in Polypropylene Manufacturing

Polypropylene producers face mounting pressure from raw-material volatility and shrinking margins. Propylene prices swing with crude and natural-gas benchmarks, creating relentless cost uncertainty for manufacturing operations. Meanwhile, massive capacity additions in Asia are driving oversupply that erodes selling prices, while ongoing logistics disruptions continue to keep supply chains unstable across global markets. AI-enabled optimization provides a concrete counterweight to these pressures. By turning live market and plant data into real-time action, data-driven models can cut feedstock waste and slash energy use. These savings flow directly to profitability, cushioning cost-per-pound when propylene spikes. The strategies ahead demonstrate how AI can steady feedstock spend, transition specialty grades with less scrap, stretch catalyst life, tighten finishing quality, and avoid costly downtime. These capabilities position your plant to compete on cost, quality, and sustainability even in a market where every cent counts. 1. Manage Volatility in Propylene Feedstock Utilization Propylene costs swing with the same forces that move crude oil and natural gas, creating relentless exposure to margin compression. Recent price fluctuations show how quickly expenses can spike, while tariff impacts compound uncertainty for import-dependent facilities. Predictive analytics built on industrial AI can ingest futures curves, regional inventories, and historical spreads to forecast short- and mid-term feedstock costs, helping you lock in contracts when economics are favorable. Machine learning inferentials calculate on-stream conversion rates and automatically adjust reactor temperature and hydrogen ratios, extracting more polypropylene from every pound of propylene. A closed-loop optimization model continuously ingests live price feeds and sensor data, recalibrating setpoints in real time. Plants adopting this strategy can achieve a reduction in raw material use and lower cost per pound of polymer produced, benefits demonstrated in operational excellence implementations. 2. Protect Margins During Specialty-Grade Production Specialty-grade polypropylene commands higher margins but demands precision that increases complexity and risk. Machine learning models excel at predicting transition curves and optimal residence time, crucial for minimizing production losses when switching material flow index (MFI) or color targets. These systems reduce off-spec material generation and ensure smooth specification changes. Advanced analytics automates lot-release decisions through predictive insights, while computer vision technology detects surface defects and color inconsistencies in real time. This combination reduces rework rates, accelerates order fulfillment, and justifies premium pricing. The result is improved profitability through reduced waste and enhanced customer satisfaction via superior product consistency. 3. Extend Catalyst Efficiency and Reduce Costs Catalyst procurement ranks among the largest variable expenses in polymer production, and demand for higher-performance materials is pushing those costs even higher. The global polypropylene catalyst market is projected to grow from $2.29 billion in 2024 to $5.15 billion by 2034. Machine learning models ease that pressure by pinpointing the best catalyst formulation for each campaign and continuously fine-tuning reactor temperature, pressure, and H₂ to propylene ratios to prevent premature deactivation. Because these systems learn from live sensor, lab, and historian data, they keep catalysts in optimal operating windows, extending active life without sacrificing product quality. Industrial AI deployments in polymer plants demonstrate that smarter setpoint control can trim reactor energy use and reduce catalyst spending. For a mid-size facility, this can translate into millions of dollars in annual savings. In parallel, predictive models estimate remaining catalyst life, allowing you to schedule replacements precisely when economic benefit outweighs risk, rather than following fixed calendars. 4. Improve Extrusion and Finishing Consistency Downstream pelletizing and finishing often erode margins because slight shifts in melt pressure, screw speed, or cooling rates snowball into off-spec product and rework. Industrial AI models maintain these variables in much tighter bands than traditional advanced process control alone. These systems continuously tune screw speed and melt pressure using real-time viscosity and temperature readings to hold the melt in its optimal window. This closed-loop approach stabilizes throughput even when upstream conditions fluctuate, eliminating disruptions that cascade into production losses. Adaptive cooling algorithms adjust water-bath or air-knife temperatures dynamically, locking in crystallinity and preventing pellet warpage. Computer-vision cameras mounted near cutters inspect every strand; when color drift or surface defects appear, the AI instantly corrects pigment feed or cooling intensity, reducing giveaway and speeding lot release. Plants applying these methods report up to 20% cuts in extrusion energy use and 1–3% rises in finished-product throughput. Multi-spectral sensor arrays analyze pellets for density and trace impurities. Machine learning cross-checks these readings against historical lab data, flagging anomalies before they reach packaging. This real-time intervention drives consistent quality, minimizes re-grinds, and protects premium pricing tied to specialty grades. 5. Reduce Downtime and Protect Asset Reliability Unplanned outages can erase days of profit in polypropylene operations, yet many plants still rely on reactive maintenance. Predictive monitoring continuously streams vibration, temperature, and pressure data from extruders, reactors, and compressors into models that flag weak signals long before failure.  A sharp rise in gearbox vibration gets detected hours sooner than traditional alarms, allowing maintenance teams to swap bearings during planned lulls rather than costly line-down events. The same anomaly detection extends to fouling-rate models for reactors and heat exchangers. By learning how heat-transfer coefficients drift, these systems predict when performance dips below economic thresholds and recommend cleaning-in-place only when needed, trimming unnecessary shutdowns.  Plants deploying predictive maintenance report significant improvements in uptime while reducing energy consumption and boosting throughput—gains large enough to protect margins even in tight markets. Turning AI Insights into Sustained Margin Gains Across polypropylene operations, these five optimization strategies work together to improve yield, sharpen quality, and lower energy intensity. AI models learn from existing historian and live sensor data, integrating directly with your DCS without requiring new reactors or extruders. Plants adopting this approach can see energy use drop by double-digit percentages and throughput climb several points, all while limiting capital investment. Imubit’s Closed Loop AI Optimization solution brings these capabilities together specifically for polypropylene manufacturing. You can begin in advisory mode, move to closed loop when ready, and verify every improvement against historian baselines. If you are seeking sustainable efficiency improvements amid feedstock volatility and rising sustainability pressures, request a complimentary plant AIO assessment to chart the fastest path to resilient margins.
Article
October, 06 2025

How to Extended Run Lengths in Delayed Coker Operations

Every extra day you keep a delayed coker online protects millions of dollars in margin and avoids the scramble of unplanned maintenance. Best-in-class operations routinely achieve extended heater runs, yet the shift to heavier crudes has pushed many units into significantly shorter cycles.  Some refineries have seen a dramatic collapse in the run length due to rapid fouling. The cost isn’t limited to decoking crews and replacement tubes; every lost day starves downstream units of feed and erodes overall refinery profitability. Whether you’re a COO, VP of Operations, plant manager, or process engineer, the following proven strategies focus on practical moves you can start today and the advanced optimization path that follows. Plants executing these steps can add days—or even weeks—of safe operation between spalls, translating into lower maintenance spend and steadier product yields. Why Delayed Cokers Lose Run Length Run length is the time between tube decoking outages. Under ideal conditions, delayed coker heaters can stay on-stream for extended periods before cleaning is required, yet many plants fall short of that target. Every unexpected shutdown erodes throughput and inflates maintenance budgets, often by millions. The dominant culprit is heater fouling, where deposits drive up pressure drop and tube-wall temperature until safe limits are reached. Fouling begins when rising film temperature thermally destabilizes asphaltenes—they precipitate, stick to rough metal surfaces, then dehydrogenate into hard coke. Contaminants such as iron and sodium accelerate the process, making heater fouling the leading unplanned-shutdown driver. Extending run length starts with dependable data: Calibrated sensors – Ensure flow, ΔP, and wall-temperature sensors are properly calibrated and in place Data collection – Maintain a historian of one-minute data Cross-functional team – Assemble specialists from operations, process, maintenance, and digital departments Baseline metrics – Document your starting point, including average run length, fouling rate, and cleaning cos,t to measure improvements against Balance Heater Passes Early & Continuously A single starved pass creates a cascade of problems. Coke builds rapidly, tube-metal temperatures spike, and the entire delayed coker shuts down unexpectedly. Uniform flow and heat flux across every coil are the first safeguards against premature shutdowns. Start each shift by comparing the pressure drop for every pass. Even a small rise pinpoints a starving coil before major issues develop. When you detect an imbalance, throttle orifice plates or slide valves until flows converge and the skin-temperature spread narrows to an acceptable range. Weekly audits of tube-metal temperatures and outlet flows keep the passes aging together. Embed automatic alerts in your distributed control system (DCS) so deviations appear before operators notice them manually. Don’t rely on a single bulk flowmeter—fouling often hides inside individual coils. Confirm that steam-to-oil splits remain even across all passes. Excess steam in one pass strips heat from another, creating the very imbalance you’re trying to prevent. Consistent, balanced firing maintains the high velocities that delay coke laydown, buying precious months of run length while larger optimization projects come online. This foundational step creates the stable operating environment that advanced systems thrive on. Avoid Counter-Productive “Obvious Fixes” When throughput starts to slip, the knee-jerk reaction is often to crank up furnace duty or push more feed through the coils. This quick fix does more harm than good. Higher firing rates spike tube wall film temperature—already the single strongest driver of coke lay-down—and extend residence time above critical thresholds, accelerating fouling in every pass. A smarter path is to treat heat input like a throttle, not a light switch. Incremental duty changes give metal temperatures time to stabilize and let you verify that skin-bulk spreads stay within safe limits. At the same time, keep mass velocity high—adequate shear scrubs nascent deposits before they anchor to the tube wall, as AFPM Q-35 guidelines demonstrate. Steam helps only within a narrow window. Optimal levels give enough gas velocity to thin the boundary layer, but not so much that backpressure balloons and drums bulge. Industry best practices pair this with uniform heat flux across burners to slow coke growth without surrendering conversion—a disciplined alternative to the obvious fix that shortens your run. Implement Predictive Monitoring for Fouling Predictive monitoring transforms raw signals from delayed coker heaters into an early-warning system that spots fouling days or weeks before throughput or tube-metal temperature forces you to spall. High-resolution data already lives in the historian, yet fouling still surprises plants because the right indicators aren’t surfaced in time. Focus first on these critical variables that move fastest when deposits begin to form: Pressure drop across each coil – Monitors flow resistance caused by deposit buildup Spread between skin and bulk temperatures – Indicates the insulating effect of coke formation Unexpected rise in oxygen at coil outlet – Signals combustion efficiency changes When trended together, these signals reveal the accelerating boundary-layer reactions that drive heater fouling and coke laydown. Historian data feeds simple inferential models, creating soft sensors that smooth out noisy instruments. Machine learning techniques then combine those inferentials with feed properties and firing patterns to forecast when any pass will hit its metallurgical limit.  A pilot typically provides sufficient validation: pull the last year of data, train the model, back-test against manual fouling inspections, and validate predictions during routine operation. Troubleshooting remains essential even with advanced models. If thermocouples read erratically, compare them with infrared imagery. If pressure drop spikes without a matching temperature rise, check for steam-to-oil imbalances or burner maldistribution.  By combining disciplined data hygiene with learning models that update as feed quality shifts, you give front-line operations a clear view of emerging fouling and the confidence to schedule cleaning on your terms rather than the heater’s. Automate Real-Time Adjustments with AI Traditional advanced process control (APC) depends on static linear-program models that break down when feed quality shifts. A Closed Loop AI solution, powered by reinforcement learning (RL), learns continuously from historian and live data and writes new set points to the distributed control system (DCS) in real time, keeping furnace duty and drum conditions on target. Governance comes built-in. Models undergo audits, operators complete courses through training programs, and transparent dashboards explain every control move. The system integrates with existing APC, preserving prior investments while documenting margin uplift and longer intervals between decokes, proof of ROI that finance teams can trust. Stabilize Upstream & Connected Units You can only squeeze so much run length out of a coker if its upstream partners are misbehaving. Temperature swings in the crude distillation unit (CDU) or vacuum tower ripple straight into the delayed coker heater, changing feed density, metals, and asphaltene loading. Each upset forces the heater to chase a moving target, accelerating fouling and trimming weeks from the campaign.  A coordination checklist keeps the whole train aligned: Lock in CDU cut points so the vacuum furnace never sees shock loads Maintain constant vacuum furnace coil-outlet temperature to avoid surges in resid viscosity Balance visbreaker recycle so the coker feed’s metals and Conradson carbon stay within design limits When these variables stay flat, heater firing can run lower, tube-metal temperatures drop, and fouling slows markedly. Advanced optimization solutions now link historian data from all three units, learning their interactions in real time and nudging set points across the network.  Instead of reacting to an upset after coke has already formed, the model trims a fraction of a degree or a few kilopascals upstream, preserving steady feed quality and buying precious days of additional run length. Extend Your Coker Run Length With These Proven Strategies Balance every heater pass early and often so no starved coil dictates the unit’s shutdown clock. Avoid the knee-jerk fix of cranking duty or feed—higher film temperatures only accelerate coke deposition. Stand up predictive monitoring that turns temperature, pressure, and ΔP data into a live fouling index you can trust.  Move from monitoring to action by allowing closed-loop optimization to adjust duty, steam, and recycle in real-time. Keep upstream crude and vacuum units steady, giving the coker a consistent, low-contaminant feed. If you’re seeking sustainable efficiency improvements, advanced optimization solutions offer a data-first approach grounded in real-world operations. Plants deploying these models recover millions in lost production while cutting emissions. As industrial AI continues to mature, the plants that close the loop fastest will set the pace for refinery optimization. Get your Complimentary Plant AIO Assessment
Article
October, 06 2025

Top Chemical Process Safety Gains From Closed Loop Optimization

Human factors remain the leading cause of industrial accidents across process operations. Many serious incidents stem from human error—lapses in judgment, fatigue-induced mistakes, or momentary distractions during critical tasks. In high-hazard environments, these errors can trigger catastrophic consequences: equipment failures, toxic substance releases, and extended operational shutdowns that damage both human lives and business viability. Closed Loop AI Optimization transforms plant safety by creating a protective layer against human error. The system continuously monitors thousands of variables, learns plant-specific behavior, and maintains parameters within safety limits—automatically adjusting operations without waiting for manual intervention. The result is a paradigm shift from reactive incident management to proactive risk prevention. The following sections examine five specific safety improvements that process plants can achieve when continuous optimization becomes an integrated part of your distributed control system (DCS) and overall safety management system. 1. Reduce Human Error in Control Decisions Advanced automation can handle the thousands of temperature, pressure, and flow adjustments that operators typically manage each shift. An AI engine trained on your plant’s historical data writes optimized setpoints directly to the distributed control system (DCS) every few seconds, maintaining critical variables within safe limits without manual intervention. This approach can transform control room operations; operators no longer need to chase alarm floods or juggle competing priorities. Consider a night-shift scenario where a reactor trends toward higher temperatures while two other units require attention. Before the high-temperature alarm activates, the system can automatically trim feed rates and increase cooling, documenting every adjustment. Instead of scrambling to recover, operators can simply review the event summary. This consistency helps eliminate fatigue-induced mistakes and preserves operational expertise in searchable records. Reducing judgment errors can lead to fewer incident investigations, more predictable staffing requirements, and less unplanned downtime, allowing your team to focus on higher-level optimization rather than constant firefighting. 2. Prevent Unsafe Operating Conditions Before They Escalate Intelligent control systems keep temperature, pressure, and composition inside the narrow bands your hazard analysis defines. High-frequency feedback from pressure sensors and analyzers feeds a controller that writes new setpoints to the distributed DCS in real time, trimming deviations before they drift beyond safe margins. That speed matters. A human may need minutes to notice a subtle pressure rise, trace its cause, and react; the algorithm intervenes within seconds, eliminating the window where an excursion can build momentum. Continuous data scrutiny spots the faint, slow trends—sluggish cooling water, creeping feed fouling—that historically foreshadow serious upsets. Runaway reactions are a prime example. By comparing heat-release curves to live reactor data, the system throttles feeds or boosts cooling the moment conditions approach critical energy-release rates, averting the chain reaction documented in runaway reaction case studies. Safety envelopes such as Maximum Allowable Working Pressure remain intact because the controller enforces hard limits and, by identifying trends, predicts when a parameter is likely to cross them, prompting proactive adjustments long before alarms ever sound. 3. Strengthen Equipment Reliability and Integrity When intelligent optimization maintains real-time control, temperature and pressure stay consistent rather than cycling between extremes. This smoother operational profile limits thermal expansion, mechanical fatigue, and vibration—the very forces that erode reactor walls, furnace tubes, and compressor seals. Continuous optimization reduces the stress that shortens equipment life, helping you avoid unexpected failures. Petrochemical units that use continuous feedback to keep pressures within design limits demonstrate this principle in action. Rather than experiencing pressure spikes that crack gaskets and flanges, these facilities maintain steady conditions that preserve equipment integrity. Modern solutions layer equipment-health data—vibration patterns, bearing temperatures, motor current—into the same optimization loop. When subtle deviations appear, the system triggers early work orders, transforming potential emergency shutdowns into planned maintenance windows. The result is extended equipment life, steadier production, and greater confidence in plant integrity. 4. Ensure Consistent Compliance With Safety Standards Keeping every valve, reactor, and vent inside its approved limits is a nonstop obligation. Smart automation turns that obligation into code. The system reads thousands of sensor points in real time, compares each value to the safe operating envelope defined under OSHA Process Safety Management and EPA Risk Management plans, then writes corrective setpoints back to the distributed control system (DCS). Because adjustments arrive within seconds, excursions that could trigger a violation never materialize. Every interaction is time-stamped and stored. The automatic audit trail arms Process Safety Engineers with ready evidence for inspections, incident reviews, and targeted training—no more piecing together paper logs. The same data underpins proactive alerts: if pressure trends toward a Maximum Allowable Working Pressure, you know before limits are breached. Plants running advanced control systems see fewer citations and lower penalties, all while protecting throughput. Intelligent automation lets you stay compliant and profitable at once. 5. Foster a Proactive Safety Culture When automated optimization handles continuous micro-adjustments throughout operations, you gain the bandwidth to focus on higher-value tasks, risk assessments, improvement workshops, and long-term reliability studies instead of chasing alarms. This shift moves operations from reacting to problems toward preventing them, a hallmark of strong safety culture highlighted by NIOSH’s proactive behavior framework. Modern optimization models learn continuously from live data and act instantly, a capability that can cut unplanned downtime significantly. Each automated move is logged and explained, so operators see exactly why a controller tightened a pressure limit or slowed a feed pump. Transparency builds trust, and those explanations become bite-size lessons that deepen understanding of process dynamics. Over time, you’re not just adjusting setpoints, you’re collaborating across maintenance, engineering, and management with a shared, data-driven view of risk. Training simulators that mirror real plant responses further embed best practices, accelerating upskilling and reinforcing a culture where anticipating hazards becomes second nature. Increase Your Plant’s Safety and Profitability Through AI Automation Intelligent automation reduces human error, maintains every variable within safe limits, alleviates thermal and mechanical stress on equipment, ensures regulatory compliance, and frees teams to focus on proactive safety. Together, these improvements create comprehensive safety benefits, each gain reinforcing the next until risks that once appeared in routine operations become rare exceptions. Because the same feedback loops that avert incidents also trim energy, stabilize throughput, and extend asset life, safety and profitability advance together. That synergy matters more than ever as regulators sharpen expectations and stakeholders demand proof that process safety is built into daily operations, not added as an afterthought. Facilities looking to capture these benefits can explore how Imubit’s Closed Loop AI Optimization solution turns real-time data into real-time action, delivering measurable improvements and typical payback in well under a year. Get a Complimentary Assessment today and see what a continuously learning model can do for the future of safe, efficient chemical processing.
Article
October, 03 2025

How AI Models Increase ROI in Oil Refinery Optimization

Energy keeps your refinery running, but it also drains the bottom line. You’re navigating shrinking margins, volatile energy prices, and tightening carbon rules simultaneously. Traditional levers—hardware upgrades, new units, major turnarounds—demand capital and downtime that many plants simply cannot spare. Industrial AI changes this dynamic entirely. With potential gains of 20-30% in productivity, speed, and revenue, along with 50% faster time-to-market and 30% cost reduction in R&D, the case for AI-driven optimization is compelling. By overlaying data-driven optimization on your existing distributed control system, advanced models can learn plant-specific behavior and adjust setpoints to optimize economic performance in real-time. The outcome is a measurable return on investment that does not require additional physical infrastructure. In the following sections, we demonstrate how this software-only approach delivers significant financial returns, all while optimizing your oil refinery’s operations.  1. Reduce Energy Consumption Across Heaters, Furnaces & Distillation Units Energy represents the single biggest variable cost in refining, with heaters, furnaces, and distillation columns consuming most of it. This is where closed-loop AI delivers its most immediate impact through minute-by-minute adjustments to fuel-air ratios, coil-outlet temperatures, and column reflux rates using live process data. AI-driven optimization can cut energy needs, costs, and carbon impacts. Even conservative improvements can translate to a significant increase in annual operating savings. The biggest wins emerge in crude heaters, catalytic-cracker main columns, and hydrotreater furnaces where heat duty peaks.  The technology learns each unit’s constraints and delivers explainable recommendations, eliminating black-box concerns while writing setpoints directly to your advanced process control system. The solution integrates with existing infrastructure, delivering typical payback within twelve months while meeting energy-efficiency mandates without new capital investment. 2. Maximize Product Yields Without New CapEx Beyond energy savings, AI models unlock additional barrels of high-value products by fine-tuning cut points, catalyst rates, and recycle ratios in real-time. Traditional linear-program (LP) models recalculate once daily, but an industrial AI layer continuously learns from live sensor streams and past sample results, updating setpoints minute by minute. In a fluid catalytic cracker, this approach shifts severity just enough to raise gasoline or propylene output when prices spike. A hydrocracker pivots between diesel and naphtha as margins move. The solution is software-only and layers onto the existing distributed control system (DCS). This approach avoids costly outages and new capital investment while capturing revenue that would otherwise slip through the cracks. 3. Improve Reliability & Extend Asset Life with Real-Time Optimization Unexpected failures rarely start with loud alarms; more often, they appear as subtle shifts in vibration, temperature, or pressure. AI models continuously compare those signals against years of operating history, identifying patterns that may precede failure and providing time to intervene without pausing throughput.  By maintaining heaters, compressors, and rotating equipment within optimal zones, these systems help smooth fluctuations in differential temperature, pressure, and flow, potentially reducing the mechanical stress that ages critical metallurgy. An unplanned fluid catalytic cracker outage can erase a seven-figure margin in a single day. AI-powered maintenance scheduling helps avoid those shocks while reducing planned downtime. With fewer emergency repairs and steadier operation, refineries can postpone capital replacements, translating improved reliability into lower maintenance costs and extended asset life. 4. Cut Carbon Emissions While Preserving Margins Carbon costs mount quickly under frameworks such as the EU ETS, California’s LCFS, and a patchwork of regional CO₂ taxes, yet shrinking crack spreads leave little room for error. The challenging economics create a critical need to attack emissions and margins simultaneously. Industrial AI addresses this dual challenge through reinforcement learning (RL) models that learn how thousands of variables interact across the entire site. In real-time, they identify optimal setpoints to meet throughput and quality targets using less fuel.  Lower natural-gas firing directly cuts Scope 1 emissions and carbon-tax liabilities while avoiding the giveaway that can follow overly conservative operating cushions. Continuous optimization also reduces flare events by stabilizing operations, keeping ESG metrics on track, and strengthening your license to operate when disclosure rules tighten further.  5. Unlock Continuous Optimization Beyond Human Bandwidth Every shift, you face a wall of data. Operators are expected to watch thousands of tags at once while juggling alarms, lab results, and market updates; a task that inevitably leads to conservative setpoints and missed margin opportunities. Continuous optimization stalls because a human can confidently adjust only a few dozen variables, often no more than once an hour. A Closed Loop AI Optimization solution turns that bottleneck on its head. Trained on historical plant behavior, the model digests real-time data from every unit and can fine-tune more than 1,000 variables in under a minute, then write new setpoints back to the distributed control system (DCS) in real time—capabilities documented in refinery operations. Because the optimization layer sees the whole facility, it also dissolves information silos. Change management remains crucial in enabling engineers to understand the model’s logic and effectively collaborate with its recommendations. The result is augmentation, not replacement—skilled staff are freed to solve complex problems while the model captures the continuous, incremental improvements that human bandwidth leaves on the table. Consider AI Optimization with Imubit for Refinery ROI Energy savings, higher yields, stronger reliability, lower emissions, and continuous, plant-wide optimization—each delivers measurable return when industrial AI runs in a closed loop. By letting algorithms continually adjust heaters, catalytic units, and utilities, you convert thousands of small decisions into millions of dollars of margin every year. Refinery margins face unprecedented pressure. Tightening regulations increases the penalty for every excess tonne of CO₂. Early adoption positions your site for sustained profitability in this challenging environment. Imubit learns your unique operating envelope and writes optimal setpoints back to the distributed control system (DCS) in real-time, sustaining benefits without requiring new equipment. To see the site-specific upside, schedule a Complimentary Plant AIO Assessment with Imubit’s experts to see how your refinery can optimize for efficiency and more margins.
Article
October, 03 2025

Your Path to a Self-Optimizing Olefins Plant

Small gaps in tuning and data integrity add up quickly in olefins operations. A single high-leverage unit can forfeit several million dollars annually when it drifts from optimal performance, with advanced process control (APC) revealing how these seemingly minor inefficiencies compound across complex production systems. Beyond lost margin, sub-optimal firing rates and compressor loads waste energy; continuous optimization can deliver reductions in fuel use and the associated CO₂ footprint. These improvements matter because the business landscape continues to tighten around process industry leaders. Volatile feedstock prices, expanding sustainability mandates, newer competitors equipped with modern automation, and experienced personnel aging out of the workforce all squeeze profitability while raising operational risk. This guide lays out a practical path to transform a conventional olefins plant into a self-optimizing operation. Each phase tackles both the technical hurdles—data quality, control systems, and AI integration, and the organizational elements such as operator trust and change management, so you can unlock efficiency and resilience step by step. Build a Solid Data & Instrumentation Foundation Nearly 70% of process industry leaders identify data quality issues—including poor contextualization and validation—as the primary barrier to AI implementation. This challenge is especially critical for self-optimizing olefins plants, where data that meets ALCOA+ standards (attributable, legible, contemporaneous, original, and accurate) forms the essential foundation. Without trustworthy signals, every optimization layer from basic process control to AI integration falters. Start with a four-part approach: Complete tag audit — Systematically evaluate all instrumentation data points to ensure accuracy and reliability Rolling sensor-calibration schedule — Implement regular, staggered calibration cycles to maintain data integrity without disrupting operations Historian redundancy for critical tags — Create backup data collection systems for essential measurements to prevent information gaps Clear data-governance ownership — Establish accountability from field transmitter to enterprise dashboard During your audit, identify “bad actor” tags—noisy thermocouples or mis-scaled inferentials—and repair, replace, or quarantine them. Build redundancy around pressure, flow, and composition measurements that anchor safety or product-quality constraints. When a flowmeter drifts, shift to a redundant element, back-calculate the offset from material balance, then update the calibration file—no shutdown required. High-fidelity data lets future AI models learn faster, adapt safely, and turn raw numbers into real-time action. Strengthen Basic Automation & Control Loops Many olefins plants operate with regulatory loops drifting, oscillating, or stuck in manual mode, quietly eroding yield and energy efficiency. Well-tuned basic control forms the foundation upon which sophisticated optimization builds. Start with a structured loop audit using objective metrics like the variability index. Focus on loops influencing furnace severity, fractionator pressures, and compressor surge margins—these carry the largest economic weight. Retune methodically with small gain adjustments, validate responses, and transition loops back to automatic operation. Track progress with focused KPIs: percentage of loops in optimal control, reduced operator interventions per shift, and improved time in control. Avoid common pitfalls, such as aggressive gain changes that spark oscillations or overlooked valve issues. Performance monitoring tools help identify oscillation patterns quickly, enabling faster corrections and building the stable foundation that AI optimization will depend on. Deploy Advanced Process Control (APC) on High-Leverage Units When you focus sophisticated process control on the units with the biggest leverage—cracking furnaces for yield, C₂/C₃ fractionators for energy, refrigeration trains for efficiency, and compression systems for stability—small moves translate into significant annual savings, often in the million-dollar range per unit. A disciplined five-step workflow keeps the project on track: Scope the economics — Define clear financial objectives and value drivers for the APC implementation Assemble and scrub data — Collect and validate historical process data from relevant operating periods Identify and validate models — Develop mathematical representations of process relationships and verify accuracy Build a living constraint matrix — Create a dynamic framework of operating boundaries that adapts to changing conditions Train operators and confirm benefits — Prepare front-line teams and verify performance improvements in live operation Because today’s controllers can function like advisories or digital twins, they outperform older “set-and-forget” model-predictive approaches, but only if you guard against common pitfalls. Model mismatch during rapid feed changes requires refreshing models on a defined cadence.  Constraint violations in transitions demand hard limits baked into the matrix. Operator pushback needs a clear rationale for every move. Performance drift over time requires monitoring KPIs and proactive retuning. Introduce AI in Advisory (“Open-Loop”) Mode Think of advisory mode as a digital co-pilot. The AI engine studies your plant data, spots hidden correlations, and suggests setpoint moves, while you remain in full command of the DCS.  The rollout starts with cleansing and organizing plant data, then training models that reflect both physics and past operations. Next, the AI runs side-by-side with your plant historian, generating recommendations but never touching the board. A dashboard shows each proposal, its predicted gain, and a confidence score; accepting or rejecting a move takes one click. Trust builds through evidence. Daily variance charts can compare AI suggestions with actual results, and early wins on non-critical circuits prove the approach works. Explainability dashboards trace every recommendation back to the underlying data, satisfying regulatory expectations for completeness and traceability while giving operators confidence in the system’s logic. This advisory phase serves as crucial preparation for autonomous operation, allowing teams to validate AI recommendations while maintaining full operational control. Move to Closed-Loop AI Optimization Transitioning to autonomous optimization requires careful preparation to ensure both safety and success. A comprehensive readiness assessment typically includes cybersecurity evaluation, change management protocols, fail-safe verification, cross-shift training, and baseline performance documentation. This systematic approach helps ensure regulatory compliance while establishing clear benchmarks for measuring improvement. Once deployed, the AI optimization solution continuously processes real-time data streams, predicts optimal operating windows, and writes refined targets directly to the DCS. The system maintains strict adherence to safety, environmental, and product quality boundaries, ensuring operational constraints are never compromised. These models function like a digital twin, creating a dynamic virtual representation that adapts as feed compositions shift or operating conditions change. Building operator confidence remains essential throughout deployment. Monitoring systems track data quality, model performance, and operational stability, with automated safeguards triggering rollback to traditional control when needed.  Expand & Institutionalize an Optimization Culture A self-optimizing plant depends as much on people as on algorithms. Embedding optimization into daily routines means every shift, engineer, and manager can push performance further. Start by forming a cross-functional optimization team that brings operations, engineering, maintenance, and leadership to one table. The council schedules recurring KPI reviews, assigns clear owners, and publishes action items, so accountability doesn’t drift with staffing changes or market swings. Culture turns into momentum through disciplined routines. Plan quarterly model retraining sessions that include control-room staff, refresh training curricula, and rotate champions across units so knowledge flows between shifts. Recognize improvements publicly because visible wins build trust faster than slide decks. Objections surface when workloads spike or budgets tighten. If you face staffing shortages, use the council to prioritize automation tasks that can free operators from manual monitoring. During downturns, link each initiative to margin protection and emissions targets to keep leadership engaged. Even small efficiency improvements can translate into meaningful carbon reductions that feed directly into ESG reports and net-zero road maps. By quantifying both financial and environmental benefits in one dashboard, you give every stakeholder a reason to keep optimization on the agenda, whatever market conditions bring. Accelerate Your Olefins Plant Optimization Journey with Imubit The six-phase path outlined here lowers risk by tackling constraints in the right order. Each step layers onto existing equipment, turning incremental investments into efficiency improvements, sustainable operations, and profit growth. You can progress sequentially, yet unifying phases under one solution accelerates every gain. That’s exactly what Imubit delivers. The technology integrates with your distributed control system, learns plant-specific behavior in real time, and writes optimal setpoints back without disrupting established safety layers. Imubit strengthens existing process control, shortens decision cycles, and keeps benefits compounding long after initial deployment. Get a Complimentary Plant AIO Assessment
Article
October, 03 2025

How AI Optimization Supports Decarbonization in Polymer Manufacturing

Polymer manufacturing generates a substantial carbon footprint, representing more than 5% of global greenhouse-gas emissions. With plastics demand continuing to climb, every percentage point of efficiency gained today prevents a much steeper emissions curve tomorrow. Several strategic approaches provide a path to emissions reduction while maintaining or improving production rates. By focusing on optimizing efficiency in energy-intensive processes, manufacturers can achieve meaningful sustainability gains.  Closed-loop AI Optimization applied to key equipment can significantly improve energy efficiency in polymer manufacturing, while intelligent load balancing and dynamic setpoint adjustments have demonstrated considerable emissions reductions across various industrial applications. Map Your Baseline: Data, Emissions & Opportunity Sizing Before you can shrink carbon intensity, you need a clear starting point. Begin by pairing your historian tags, lab quality results, utility meter readings, and published emissions factors to calculate kilograms of CO₂e per metric tonne of polymer. Because nearly 75% of lifecycle emissions occur upstream of polymerization, a robust baseline highlights which operating windows matter most for decarbonization. The baseline mapping process follows four repeatable steps: Collect high-frequency data from the DCS and historian Cleanse, reconcile, and align units, filling sensor gaps with inferentials Visualize flows, fuels, and electricity use, then convert to CO₂e Lock this snapshot as the reference set for model training and later benefit tracking Expect hurdles along the way—mis-calibrated instruments, mislabeled units, or missing utility meters can complicate data collection. Focus first on heat-intensive assets such as steam crackers, furnaces, and compressors, then verify data accuracy through periodic sensor checks and cross-lab correlations. Validating one high-impact production line builds confidence and reveals quick, scalable abatement opportunities that can be replicated across your facility. Optimize Energy Use in Heat-Intensive Equipment Furnaces and compressors dominate a polymer plant’s energy bill, yet their setpoints often drift as feed, ambient conditions, and equipment health change. This drift creates inefficiencies that compound over time, but Closed Loop AI Optimization can address these challenges by continuously adjusting fuel, airflow, and pressure to the most efficient operating point without sacrificing quality or throughput. The journey to optimal energy performance unfolds in four stages: Data preparation: Map historian tags, sample results, and utility meters, then reconcile units across systems Model training: Feed cleansed data to the AI solution so it learns the cause-and-effect relationships specific to your operations Advisory mode: Surface recommended setpoints for operator review and validation before implementation Full deployment: Enable the model to write adjustments directly, with safety guards from existing advanced process control (APC), providing operational confidence Once active, the solution ingests thousands of real-time signals per minute, predicts energy demand seconds ahead, and updates targets accordingly. You can track impact through lower specific energy consumption and by multiplying saved megawatt-hours by your site’s CO₂e factor, demonstrating both cost and carbon benefits while maintaining stable production rates. Minimize Flaring and Process Variability Flaring represents a significant carbon contributor in polymer manufacturing, arising from both routine operations and emergency processes designed to burn excess gases safely. These activities not only release substantial carbon emissions but also signal inefficiencies in the production process. Through predictive anomaly detection, AI can anticipate deviations minutes before they lead to process disruptions, providing operators with crucial lead time to make adjustments. The integration of machine learning algorithms with advanced process control systems enables real-time monitoring and adjustment of operational parameters, providing a seamless response to potential disturbances.  Beyond lowering emissions, these AI-driven adjustments stabilize production processes, ensuring consistent product quality, maximizing throughput, and reducing the volume of off-spec batches. Key metrics for assessing success include reductions in flaring frequency, savings in CO₂ emissions, and improvements in product quality. By finely tuning model sensitivity, manufacturers can balance the risk of false alarms against the possibility of missed process upsets, ultimately optimizing both financial and environmental performance while enhancing overall plant efficiency. Improve Feedstock Conversion Efficiency When ethane, propane, or naphtha slip through your systems unconverted, you’re burning cash and generating unnecessary CO₂. Industrial AI can close that gap by training multivariate models on years of plant data to predict conversion and purity, then continuously updating reactor, column, and compressor setpoints to keep operations in the sweet spot. A typical implementation starts with defining yield KPIs, running what-if simulations against feed and catalyst scenarios, and weighting every recommendation against real-time economics before sending optimized setpoints to the DCS.  This approach can lift product yield in ethylene fractionation while trimming energy demand through tighter reflux and reboil control. In propane/propylene separation, advanced process optimization and hybrid technologies have also demonstrated utility consumption reductions. Higher conversion efficiency delivers a triple benefit: fewer flares, lower steam consumption, and a measurable drop in carbon intensity per metric tonne of polymer—all achieved without new equipment or extended downtime. The technology creates a direct path to both margin improvement and emissions reduction. Extend Catalyst and Asset Life Keeping reactors in their optimal zone for temperature, pressure, and impurity levels is the surest way to slow catalyst deactivation. Industrial AI watches thousands of live data points, learning the subtle patterns that precede coking or poisoning and correcting setpoints through the DCS before damage accelerates.  Plants using this closed-loop approach see energy improvements alongside longer catalyst cycles, because stable heat profiles curb the high-temperature spikes that shorten run length. The same models surface early fouling trends, prompting inspections or wash steps well before differential pressure forces an outage. This proactive approach means fewer unplanned shutdowns, less flaring, reduced giveaway, and lower embedded carbon from replacement materials. Effective deployment pairs real-time health dashboards with periodic model refreshes. Ignoring small drift signals or clinging to outdated training data can erase these improvements, making ongoing model maintenance essential for sustained benefits. Build a Step-by-Step Decarbonization Roadmap with AI Once you identify high-impact emission sources, the next move is charting a structured path that links incremental AI deployments to measurable carbon savings. A phased maturity model keeps the effort manageable while building toward enterprise-wide impact. The roadmap unfolds in five stages, each building credibility and funding for the next: Data readiness and baseline establishment – Gather historian tags, utility meters, and emissions factors, then reconcile gaps so models can learn from a trusted foundation. Advisory analytics and pilot applications – Deploy an AI solution in advisory mode on one furnace or compressor to establish proof of concept and build organizational confidence. Closed-loop control implementation – Grant the model write access to the DCS under clearly defined safety limits, converting recommendations into real-time action. Multi-unit optimization – Connect adjacent units so the model can balance trade-offs across reactors, recovery towers, and utilities. Enterprise-wide carbon optimization – Expand to sister plants, layering economic weighting to prioritize the lowest-carbon, highest-margin operating envelopes. Choose your starting point by weighing data accessibility, energy intensity, and organizational appetite for change. Common obstacles like data silos, legacy instrumentation, and workforce skepticism dissolve through transparent model validation and cross-functional training. Each successful phase earns credibility and resources for the next, creating an accelerating cycle where efficiency improvements drive both profitability and decarbonization gains. Your Next Move Toward Net-Zero Polymer Production Harnessing AI in polymer manufacturing offers transformative benefits: reduced energy consumption, minimized flaring, enhanced yield, and extended catalyst longevity. Embracing these technologies doesn’t just pave the way for decarbonization—it ensures sustained profitability by simultaneously meeting sustainability goals and enhancing your bottom line without sacrificing margins. To begin this journey, consider starting with applications that offer high-impact results with low barriers to entry. Assess your current operations to identify areas where AI-driven enhancements could deliver the greatest benefit, then develop pilot projects in these zones to demonstrate tangible results before scaling initiatives plant-wide. This strategic approach not only mitigates risks but showcases AI’s potential to drive significant progress in decarbonization efforts. Balancing sustainability with profitability through artificial intelligence isn’t just a possibility—it’s an opportunity waiting to be seized. The path to net-zero polymer production runs through smarter operations, and that journey starts with your first AI deployment. Discover how a unified AI model can transform both sustainability metrics and operational performance.
Article
October, 03 2025

Polymer Production Challenges Solved by Closed Loop AI

Energy and quality inefficiencies steadily erode profitability in polymer facilities. Process heating, cooling, and compression represent substantial portions of variable operating expenses, while off-spec product creates costly rework and delivery delays.  Traditional advanced process control (APC) struggles to address these challenges effectively, leaving significant value untapped, including potential average throughput increases of 1-3% and 10-20% reductions in natural gas consumption. Closed-loop artificial-intelligence optimization closes that gap. Reinforcement learning (RL) algorithms study thousands of historical campaigns, listen to live sensor feeds, and write optimal setpoints back to the distributed control system (DCS) in real-time.  The result is a self-tuning operation that continuously balances throughput, quality, and energy, without waiting for lab sample results or manual retuning. We’ll take you through five challenges that AI can address, all while enhancing your plant’s efficiency with what you already have.  1. Slash Off-Spec Production Off-spec polymer can quietly erode margins, often accounting for 5–15% of total output. Process drifts, fouling on reactor walls, fluctuating monomer purity, and the hours-long lag between sample results and control moves all conspire to push quality outside tight customer windows. Traditional advanced process control relies on static models, so every unexpected disturbance forces operators to choose between steady production and costly rework. Closed-loop AI optimization replaces static equations with data-driven models that learn as conditions evolve. Streaming sensor data feeds reinforcement learning algorithms that write optimized setpoints back to the distributed control system in real-time, correcting deviations before an entire batch slips out of spec. Because the models capture nonlinear interactions among temperature, feed ratios, and catalyst activity, they maintain grade consistency even when raw-material quality swings. Field deployments on polymer reactors show greater yield improvement compared with traditional control methods, translating into fewer waste reprocesses and more reliable deliveries for customers.  2. Tame Feedstock Variability Beyond off-spec challenges, fluctuations in monomer quality, impurity content, and composition can significantly impact key polymer properties such as melt flow index and density. These variations often disrupt consistency, leading to production disruptions and necessitating emergency grade changes. Traditional systems struggle to adapt quickly to such changes, causing inefficiencies in maintaining polymer quality. AI-driven systems continuously update models with real-time data, enabling automatic adjustment of setpoints to uphold grade consistency. This capability not only stabilizes production but also enhances supply chain planning, resulting in consistent yields with fewer disruptions. Moreover, these systems excel in handling unconventional feedstocks like bio-based or recycled materials, which tend to have less predictable properties compared to their petrochemical counterparts. By learning from actual plant data rather than idealized assumptions, AI systems reduce process instability and minimize by-product generation. This dynamic adaptability ensures that polymer production can meet stringent quality specifications even amid varying feedstock conditions, fostering both economic and operational resilience. 3. Cut Energy Intensity (and CO₂) Energy keeps polymer reactors, compressors, and chillers humming, but it also eats up a significant portion of a plant’s variable expenses. Traditional control tools juggle throughput, steam, and power with static models, forcing you to accept crude trade-offs: push rate and watch utilities spike, trim utilities, and risk off-spec product. Closed-loop AI takes a different approach. By learning from live sensor data and historical plant performance, the model continuously balances dozens of interacting variables. It can widen operating envelopes when conditions are favorable and tighten them the moment raw-material quality or ambient temperature shifts. The result is a leaner kilowatt-hour and steam footprint—plants can expect lower energy-per-pound consumption, without sacrificing throughput or quality through operational excellence strategies. Lower energy use directly translates into reduced Scope 1 emissions and simpler compliance reporting. Cutting waste heat and fuel burn also frees capacity in utility systems, giving you more room to chase production targets while meeting decarbonization goals. 4. Extend Catalyst Life & Reactor Stability Catalysts sit at the heart of polymer production, and a premature change-out can wipe out the margin on an entire campaign. Yet fouling layers, temperature excursions, and unpredictable monomer ratios often push a high-value catalyst toward deactivation long before its design life. Traditional control systems respond to disturbances only after they appear, so you still face mid-run quality swings, unplanned cleanouts, and lost throughput. Reinforcement learning continuously learns from streaming reactor data and writes new setpoints back to the control system in real time. By smoothing thermal profiles and precisely metering co-monomer and catalyst feeds, the model shields active sites from hot spots and impurity spikes. When early indicators of poisoning emerge, the algorithm adjusts solvent ratios or residence time minutes—not hours—before damage occurs. With autonomous reactor control, you can expect longer production stretches and fewer emergency shutdowns; each extra day of catalyst life delivers direct savings in material costs and maintenance labor. This approach can maintain catalyst viability through challenging high-temperature conditions without drifting off spec, demonstrating the stabilizing power of closed-loop control.  Plants feeding sensor data from every batch into their learning engines can scale new recipes in days and maintain steady reaction rates across months-long campaigns. These improvements translate into higher throughput, lower waste, and a more predictable supply chain, exactly what you need to grow profits while meeting demanding customer specs. 5. Guarantee Compliance & Customer Specs As the benefits of improved process control compound, regulators now demand traceable quality records, while customers expect melt index or density to stay within a narrow window. Traditional workflows rely on lab sample results that arrive hours after the polymer has already left the reactor, so operators often learn about deviations too late. Closed-loop industrial AI changes that timeline. By combining high-frequency sensor data with inferential quality predictors, the models estimate critical properties in real time and write setpoints back to the control system. Each micro-adjustment keeps grade properties on target without the giveaway that comes from conservative safety margins. Because every control move and quality prediction is time-stamped, the same platform automatically builds a digital audit trail. Auditors gain immediate access to records, and customers can review run-by-run performance charts instead of waiting for spreadsheets. Unlock Greater Polymer Production Value with Closed Loop AI  Closed-loop AI addresses the five persistent constraints that erode polymer margins—off-spec production, feedstock swings, energy intensity, catalyst wear, and tightening compliance requirements. Recent deployments have shown yield improvements and lower energy use, converting accepted operational costs into measurable savings while maintaining specification consistency. Imubit represents this evolution from predictive insight to real-time action. Polymer producers ready to verify the impact can request a plant assessment. The engagement benchmarks current performance, identifies immediate optimization targets, and outlines a clear path to plant-wide scale. Connect with an Imubit specialist to start charting your own improvement curve.
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About 50-60% of our senior control room operators are going to retire in the next couple of years. It’s huge for us to be able to actually train them on something in open loop and let them play around and make mistakes and learn.
Bryan Cook of Ash Grove Cement speaking at podium

Bryan Cook

Senior Corporate Automation Engineer – Ash Grove Cement Company

Working with Imubit allows us to leverage cutting-edge AI to optimize our renewable processes while minimizing our carbon footprint.

Erika Wikström

SVP of Technology – Preem

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