Improving manufacturing throughput has traditionally meant spending money—on new equipment, infrastructure upgrades, or expanded lines. In industries like refining, petrochemicals, and specialty chemicals, this often means tens (or hundreds) of millions of dollars in capital expenditure (CapEx), years of planning, and significant operational disruption.
But this is now changing.
Today, leading manufacturers are increasing throughput not by adding more, but by doing more with what they already have. The key enabler? AI-powered process optimization.
Rather than layering on more infrastructure, modern AI platforms mine existing process data to identify inefficiencies, predict system behavior, and automatically optimize plant performance in real time.
The results are compelling. According to McKinsey, industrial processing plants that have adopted AI report a 10–15% increase in production and a 4–5% uplift in EBITA without the need for major capital investment.
In this article, we’ll explore best practices to unlock higher throughput using AI and without requiring new CapEx. We’ll also examine what success looks like, how to avoid common pitfalls, and how to ensure your teams are equipped for long-term results.
The CapEx Problem In Traditional Process Debottlenecking
For decades, plants have addressed throughput constraints with capital investments like upgrading heat exchangers or compressors, installing new process equipment, retrofitting control systems, and building parallel production lines.
But this approach is:
- Expensive: Millions of dollars per project
- Slow: Often takes 12–36 months to plan and execute
- Risky: Hurdle rate must overcome the cost of operational disruption
- Inefficient: Often yields marginal gains without addressing root inefficiencies
What’s worse—many of these upgrades under-deliver because they fail to solve hidden operational inefficiencies. Debottlenecking one section of the process often comes with the side effect of identifying the next major throughput constraint.
A New Approach: AI-Driven Optimization Without CapEx
Modern AI solutions do not require changes to physical assets. Instead, they unlock capacity by making smarter use of what already exists within the plant.
These systems start by leveraging historical process sensor data. With this foundation, AI platforms create dynamic, self-learning models of plant behavior, unique to your process, that evolve based on real-time inputs.
This intelligence allows the AI to identify hidden constraints and nonlinear interactions between units—factors that are often too complex for traditional systems or manual analysis to detect. Once these insights are uncovered, the AI makes autonomous setpoint adjustments while respecting established process and safety limits.
The result is a system that continuously optimizes plant performance and maximizes throughput—even under constantly changing conditions—without the need for investment in new equipment.
Best Practices To Improve Throughput Without CapEx
Here’s how leading manufacturers are doing it:
Start With A High-Impact, Low-Risk Use Case
Success with AI starts by choosing the right initial use case. Ideally, this use case should have a measurable impact on throughput or margin. It should also focus on a well-instrumented and data-rich process area to ensure the AI has enough information to work with. The chosen process should contain a known bottleneck or critical constraint. Most importantly, it should not interfere with regulatory requirements or safety-critical operations.
For instance, manufacturers can increase throughput by maximizing feed rates to a crude distillation unit, while still keeping furnace coil outlet temperatures within safe operating limits. In another case, reactor units can be optimized to push higher throughput without breaching critical parameters such as conversion efficiency or selectivity, which are essential for product quality and yield.
Similarly, in compression systems, operators can enhance throughput by safely approaching surge limits without crossing into unstable zones that risk equipment integrity. These targeted use cases allow manufacturers to unlock hidden capacity within existing infrastructure, without compromising safety, reliability, or quality.
When done right, early success in these specific areas builds momentum, earns internal trust, and lays the foundation for broader AI-driven optimization across the plant.
Think In Terms Of System Constraints, Not Equipment Limits
Operators often focus on local constraints such as furnace duty, reactor temperature, or column pressure. While these parameters are important, they may be camouflaging the system’s true throughput limit. This narrow focus can lead to missed opportunities for performance gains across the broader process.
AI helps uncover hidden or dynamic constraints by analyzing how multiple control loops interact, tracking real-time trends alongside delayed effects, and evaluating constraint trade-offs that are too complex for human operators to assess manually.
For example, pushing a distillation column to operate harder might seem like a way to increase throughput. However, doing so can reduce product yield downstream. AI identifies these complex cause-and-effect relationships and determines the optimal operating point—balancing competing constraints to maximize throughput across the entire system.
Empower Operators With Transparent, Explainable AI
The best AI systems are not black boxes. They function as collaborative copilots that work with operators, not around them. Rather than replacing human judgment, these systems enhance it by providing actionable insights and clear recommendations.
To build this trust, an AI platform should explain its recommendations in plain language that operators can easily understand. It should show real-time performance improvements and the impact on key performance indicators (KPIs), so teams can see the value it delivers.
A familiar and user-friendly interface also plays a critical role. Operators are more likely to engage with systems that align with their daily workflows and don’t require steep learning curves. In the early stages of deployment, it’s important that the platform supports human-in-the-loop control, allowing operators to validate decisions before automation takes full control.
When operators trust the system, they are more likely to use it actively. This leads to higher adoption rates and more consistent, measurable results across the plant.
Use Continuous Learning To Keep Up With Change
Process environments are dynamic. Feedstock changes, ambient conditions vary, and production targets shift. Static models fall short.
AI platforms should adapt continuously to dynamic plant conditions. This begins with the ability to retrain optimization models using real-time data, ensuring that decisions remain accurate as operating environments evolve. As process variables, feedstock quality, or production goals shift, the platform must also automatically recalibrate its constraints and boundaries to stay aligned with operational priorities.
Equally important is the system’s ability to self-diagnose. Advanced AI platforms can detect when their models begin to drift—whether due to changing conditions, equipment degradation, or unexpected anomalies—and correct themselves without requiring manual intervention. These capabilities help maintain optimization accuracy over time, ensuring that throughput gains are not just achieved but sustained.
Measure, Share, And Scale
After deploying AI in one area of the plant, it’s important to establish a feedback loop to maximize value and support scalable success. Begin by measuring the gains, such as percentage increases in throughput or daily revenue uplift, to quantify impact.
Document the key learnings and outcomes to create internal playbooks that can guide future deployments. Then, identify the next area or unit where AI can be applied, focusing on similar systems or bottlenecks.
Look for patterns—like repeatable configurations, recurring constraints, or common control challenges—that can help accelerate and streamline expansion across the plant.
Why Domain Expertise Still Matters
Generic AI platforms often miss the critical nuances of complex manufacturing processes. This can result in inaccurate recommendations that not only fail to improve performance but may also introduce safety risks or erode operator trust, leading to poor adoption. To avoid this, it’s essential to choose a partner with deep domain expertise.
Look for a provider that understands the unique characteristics of chemical and energy operations and has a track record of success across refineries, chemical plants, and polymer facilities. Equally important is a co-development approach—one that actively involves your engineers and operators in the design and implementation of the solution.
This ensures the AI system is grounded in real-world plant knowledge, making it more likely to be accepted, understood, and used effectively.
Here’s What Makes Imubit Different
It’s not enough to deploy AI—you need AI built for your industry. Imubit’s Closed Loop AI Optimization (AIO) technology was built by process engineers and AI scientists to solve one problem: real-time, unit- and plant-wide optimization in complex manufacturing environments.
We help plants:
- Improve throughput and margin—without CapEx
- Run closer to economic optimum—autonomously and continuously
- Free up operator and engineer time from constant firefighting
- Achieve measurable impact in weeks, not years
Our solution integrates with your existing DCS, APC, and historian—no new sensors or control systems needed.
If you’re facing throughput constraints but want to avoid the cost, time, and risk of a capital project, Imubit can help. Book a complimentary AIO assessment to see real-world examples and identify where AI could unlock capacity in your plant.
