Maximize On-Stream Factor Through Smarter Process Control

A single unplanned shutdown can wipe out a month of margin improvements. The cascade effect multiplies the damage: downstream units destabilize, off-spec product accumulates during restart, and labor costs spike while maintenance teams work overtime to restore operations. In chemical and polymer manufacturing, protecting uptime has become the critical lever for competitive performance.

The opportunities are substantial. McKinsey research shows that operators in industrial processing plants can achieve production increases of 10–15% through AI-enabled optimization. Realizing these improvements means moving beyond calendar-based maintenance toward predictive, condition-based strategies that extend equipment run length while protecting asset integrity.

The Hidden Cost of Conservative Maintenance

Traditional maintenance strategies systematically prevent chemical plants from maximizing on-stream factor. The approaches share a fundamental misalignment: optimizing for maintenance cost rather than production uptime.

Calendar-based preventive maintenance creates significant operational constraints. When maintenance teams shut down equipment based solely on elapsed time rather than actual condition, they interrupt production runs that could safely continue. The economic penalty is twofold: production losses during unnecessary shutdowns, plus accelerated wear from repeated startup-shutdown cycles. Equipment that undergoes frequent cycling experiences thermal stress, mechanical fatigue, and seal degradation that continuously running equipment avoids.

These approaches encounter several critical limitations. Threshold-triggered alarms detect equipment degradation only after problems have progressed significantly, leaving minimal time for proactive response. Calendar-based schedules assume predictable, age-related failure patterns, yet most process equipment experiences random failures that fixed schedules cannot anticipate. Siloed monitoring creates gaps between process conditions, maintenance history, and equipment health indicators that prevent holistic optimization. And uncertainty about actual equipment condition forces conservative run length limits that leave production capacity unrealized.

Without condition-based insights that reveal actual equipment state, plants cannot systematically extend run lengths while maintaining safe operation. Modern advanced process control addresses these limitations through continuous monitoring that transforms maintenance from reactive firefighting to strategic intervention.

From Calendar-Based to Condition-Based

AI-powered process control addresses the fundamental limitation of traditional approaches: the inability to process real-time operational data quickly enough to enable preventive action. Where manual inspections and threshold-triggered alarms require hours or days to detect equipment degradation, machine learning models analyze streaming sensor data continuously. They identify early degradation patterns and predict remaining useful life, extending warning windows from hours to weeks.

Digital twin technology creates virtual replicas of critical equipment that predict fouling, catalyst deactivation, and degradation rates under current operating conditions. These predictive models enable extended operation beyond traditional conservative schedules by quantifying actual equipment state rather than relying on time-based assumptions.

This predictive capability transforms maintenance planning fundamentally. Instead of waiting for alarm thresholds or calendar dates, operations teams receive advance notice of developing issues with sufficient lead time to schedule interventions during planned production windows. Plants can align maintenance activities with natural production cycles, feedstock transitions, or seasonal demand fluctuations rather than responding to emergency failures that force unplanned shutdowns during peak production periods.

Critically, AI optimization enhances operator judgment rather than replacing it. Condition-based insights allow experienced personnel to validate recommendations against their process knowledge before adjusting run schedules. Within validated operating envelopes, these capabilities extend the time between required interventions while maintaining the safety margins that protect both equipment and personnel.

Predictive Capabilities That Extend Run Length

Industrial AI delivers capabilities that traditional threshold-based monitoring cannot match. Machine learning models detect early degradation patterns and predict remaining useful life with probabilistic confidence intervals through several complementary analytical approaches.

• Anomaly detection uses unsupervised machine learning to establish baseline operational patterns, then detects subtle deviations indicating emerging equipment degradation before traditional alarm thresholds are breached.
• Health scoring combines process conditions, maintenance history, and operating severity to generate real-time scores for critical assets, enabling risk-based maintenance prioritization.
• Pattern recognition through multi-variable analysis identifies correlation changes where individual parameters appear normal but their relationship indicates developing problems.

Consider heat exchanger fouling in a chemical reactor cooling system. Traditional monitoring tracks outlet temperature against a fixed alarm setpoint, triggering only after fouling has progressed enough to raise temperature beyond threshold. Anomaly detection models instead analyze the relationship between flow rate, inlet temperature, outlet temperature, and pressure drop simultaneously. When this multi-variable pattern begins shifting, even while individual parameters remain within normal ranges, the system alerts operators to emerging fouling days or weeks before traditional alarms would trigger.

This extended warning window enables maintenance planning during scheduled production transitions rather than forcing emergency shutdowns during critical campaigns.

Where the Value Shows Up

The business case for AI optimization in chemical operations rests on documented performance improvements across multiple value streams. According to BCG research, early adopters of AI in manufacturing achieve 14% savings on addressed costs.

Asset reliability improves as predictive intervention replaces reactive maintenance. For continuous process plants where equipment cleaning cycles require extended shutdowns, extending average run length through condition-based optimization delivers notable improvements in annual on-stream factor. Each avoided shutdown preserves production time while eliminating startup material losses, off-spec production during stabilization, and the labor costs associated with turnaround execution.

Maintenance cost avoidance represents a secondary but substantial benefit. When plants extend run lengths through predictive intervention, they reduce the frequency of expensive turnarounds while simultaneously decreasing emergency maintenance premiums. Planned interventions during scheduled windows avoid overtime labor costs, expedited parts shipping, and contractor mobilization fees that emergency repairs demand.

Margin protection through improved product quality represents another critical value stream. Unplanned shutdowns force rapid process destabilization followed by restart sequences that generate off-spec product. A polymer plant producing specialty grades cannot sell transition material at prime pricing, converting high-margin production into low-margin commodity sales or waste disposal costs. By extending stable run periods and eliminating unplanned trips, AI optimization protects product quality consistency that calendar-based approaches systematically compromise.

Building Confidence Through Progressive Deployment

Successfully deploying AI optimization requires addressing both technical infrastructure and organizational readiness. Chemical plants considering advanced control implementation should evaluate existing data infrastructure, instrumentation quality, and control system integration capabilities.

Machine learning models require sufficient historical data spanning normal operations, process upsets, equipment degradation cycles, and maintenance events to learn accurate predictive relationships. Plants with comprehensive historian systems capturing high-frequency sensor data from critical equipment possess a deployment advantage. However, the data foundation need not be perfect to begin. Plants can start with existing sensor networks and improve coverage over time as the value of additional data points becomes clear.

The path to autonomous optimization does not require immediate closed loop implementation. Many chemical plants begin in advisory mode, where AI models provide recommendations while operators retain full control over all process adjustments. Significant value accrues at this stage through enhanced equipment health visibility, faster troubleshooting during process upsets, and accelerated workforce development as operators learn to interpret AI-generated insights.

As teams build confidence through demonstrated prediction accuracy, they progressively enable supervised automation where AI suggestions require operator approval before implementation. Eventually, plants transition to full closed loop optimization within validated operating envelopes. This journey approach reduces implementation risk while capturing value at each step, recognizing that workforce transformation and organizational trust-building require time.

How Imubit Maximizes On-Stream Factor in Chemical Operations

For operations leaders seeking to protect uptime and extend equipment run length, Imubit’s Closed Loop AI Optimization solution addresses the core limitations of traditional control approaches. The technology combines deep reinforcement learning (RL) with real-time process data to continuously optimize operations and improve performance over time.

Unlike conventional APC solutions that require extensive retuning as conditions change, the AIO solution learns directly from historical plant data to deliver sustained performance improvements. The technology delivers value immediately in advisory mode through enhanced equipment health visibility, faster troubleshooting during process upsets, and accelerated workforce development. When operating in closed loop, it writes optimal setpoints to the control system in real time. By continuously adapting to feedstock variations, catalyst aging, and seasonal conditions, Imubit captures improvements that conservative manual approaches leave unrealized.

Get a Plant Assessment to discover how AI optimization can maximize on-stream factor while protecting equipment integrity in your chemical operations.