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Complete Guide

Model Predictive Control Software: A Complete Guide to Smarter Industrial Process Optimization

Model Predictive Control (MPC) software has become one of the most consequential technologies in advanced manufacturing. Deployed across refineries, chemical plants, polymer facilities, and food processing operations worldwide, MPC enables manufacturers to push processes harder, tighter, and more consistently than traditional control strategies allow—while respecting the physical and operational limits that keep plants running safely.

Industrial worker performing precision metal fabrication

01

What Is Model Predictive Control (MPC) Software?

Model Predictive Control is a form of advanced process control that uses a mathematical model of the process to predict future behavior and calculate the optimal sequence of control moves needed to achieve a desired outcome—while keeping the process within defined constraints.

Unlike conventional control strategies that react to what has already happened, MPC software looks ahead. At each control interval, it simulates how the process will behave over a defined prediction horizon, identifies the best sequence of adjustments, applies the first move, then repeats the entire calculation at the next interval. This receding-horizon approach allows MPC to anticipate process dynamics rather than simply respond to them.

A Brief History

A Brief History

MPC was developed in the late 1970s and early 1980s by engineers working primarily in oil refining and petrochemicals—industries where multivariable interactions, slow process dynamics, and tight product specifications made traditional PID-based strategies inadequate. The earliest commercial implementations, including IDCOM and DMC (Dynamic Matrix Control), demonstrated that feed-forward, constraint-aware, multivariable control was not only possible but commercially transformative. By the 1990s, MPC had become the standard architecture for advanced process control in continuous manufacturing.

How MPC Differs from Traditional PID Control

PID (Proportional-Integral-Derivative) controllers work well for single-loop control: one controlled variable, one manipulated variable, and a relatively simple process response. The vast majority of control loops in any plant are PID loops, and they perform their intended function reliably.

The limitations of PID become apparent when processes involve multiple interacting variables, slow or variable dynamics, hard constraints, or optimization objectives that extend beyond simple setpoint tracking. In those situations, individual PID loops—even when well-tuned—work in isolation. They cannot coordinate actions across multiple manipulated variables simultaneously, and they have no mechanism for anticipating future process behavior.

MPC addresses these limitations directly. A single MPC application can simultaneously manage dozens of controlled variables and manipulated variables, account for process interactions, honor operating constraints, and drive the process toward an economic optimum—all within a single coordinated control calculation.

Engineer working with electronic process control components

Whether a facility is evaluating MPC for the first time or has had a system in place for years, Atlas Prediction Control helps manufacturers understand what their process control architecture is actually capable of—and what it would take to close the gap between current performance and achievable performance.

02

Why Manufacturers Use Model Predictive Control Software

The business case for MPC is well established across decades of industrial applications. When implemented and maintained correctly, MPC delivers measurable improvements across the most important operational and financial metrics in manufacturing. Atlas Prediction Control helps manufacturers achieve these benefits in practice—not just on paper.

Industrial worker performing precision metal fabrication

Operational Performance

Performance is created at the operating edge.

04

Reduce Energy Consumption

Energy is typically the largest variable cost in continuous chemical and petrochemical processes. MPC can simultaneously optimize process conditions to minimize energy input while maintaining product quality and throughput targets. In distillation, for example, MPC can reduce reboiler duty while keeping separation quality within specification—an optimization that is extremely difficult to execute manually or with decentralized PID control.

Atlas Prediction Control helps manufacturers identify whether their existing MPC application is actively pursuing energy reduction as an objective, or simply maintaining setpoints that were defined during commissioning and never revisited against current energy costs and production priorities.

02

Reduce Process Variability

Variability is the enemy of efficient manufacturing. It forces conservative operating targets, reduces on-specification product yields, increases raw material consumption, and creates unpredictable demand on downstream operations.

MPC reduces variability by coordinating control actions across multiple variables simultaneously and by responding to disturbances before they propagate through the process. Where a bank of PID controllers reacts to each deviation independently—sometimes working against each other in the process—MPC calculates a coordinated response that accounts for how adjustments in one variable will affect every other variable in the system.

If variability has been increasing in a facility that already has MPC installed, Atlas Prediction Control can diagnose whether the root cause is model degradation, constraint configuration, tuning drift, or a change in process dynamics—and execute the corrective action needed to restore performance.

03

Improve Product Quality

In industries where product specifications are narrow and off-specification product represents significant cost—polyethylene, specialty chemicals, pharmaceuticals, food ingredients—MPC provides a direct path to quality improvement. By holding key quality variables tighter to target, transitions between product grades become faster and cleaner, on-spec yield increases, and the amount of downgraded or reprocessed material decreases.

01

Increase Throughput

Every continuous manufacturing process operates within a set of constraints—equipment limits, product specification limits, safety limits, and environmental limits. Traditional control strategies typically maintain operating points comfortably inside those constraints, leaving a cushion that protects against variability but sacrifices production rate.

Atlas Prediction Control works with manufacturers to identify specifically where operating points can be safely pushed closer to constraints, and to validate that the MPC application is actually configured to capture that opportunity. Many facilities leave throughput on the table not because the potential isn’t there, but because the controller was never optimized to pursue it.

◇

Operate Safely Within Constraints

MPC is inherently constraint-aware. Engineers define hard and soft constraints representing equipment limits, safety boundaries, product specification ranges, and environmental permit limits, and the MPC solver explicitly respects those constraints in every control calculation. This is a fundamental architectural advantage over PID control, where constraint management is typically handled through separate logic—alarms, interlocks, operator intervention—that operates reactively rather than proactively.

03

How Model Predictive Control Software Works

Understanding what MPC actually does inside the controller helps engineers evaluate applications, diagnose performance issues, and communicate the technology’s value to plant management. It also reveals exactly where engineering expertise—and Atlas Prediction Control’s work—has the greatest impact.

Engineer working with process control electronics

From Plant Data to Optimized Action

MPC continuously repeats the same disciplined cycle: measure, predict, optimize, execute, and validate against the process as it actually behaves.

MPC continuously repeats the same disciplined cycle: measure, predict, optimize, execute, and validate against the process as it actually behaves.

01

Collect Plant Data

02

Predict Future Behavior

03

Optimize Control Actions

04

Operate Within Constraints

05

Adjust in Real Time

Repeat at the next interval

Receding Horizon

01

01

01

Foundation

Building a Process Model

Every MPC application is built on a process model—a mathematical representation of how the process responds to changes in its inputs. In most industrial MPC applications, this model takes the form of a step response or impulse response model: a set of coefficients that describe how each controlled variable responds over time to a unit step change in each manipulated variable.

These models are identified from plant test data collected during a dedicated identification campaign. Engineers design and execute a series of step changes or pseudo-random binary sequences across the manipulated variables, record the process responses, and use system identification software to extract the dynamic model. The quality of this identification test—its duration, the size and frequency of the moves, the ability to separate the effects of individual variables—directly determines the quality of the resulting model.

Common Mistake: Rushing the model identification campaign to minimize production disruption is one of the most common reasons MPC applications underperform from day one. A high-quality process model is the foundation of everything the controller does. Compromising on identification quality is a cost that compounds over the entire life of the application. Atlas Prediction Control designs and executes identification campaigns with the rigor needed to build models that hold up over time—not just pass initial commissioning checks.

02

02

02

Prediction

Predicting Future Process Behavior

At each control interval, the MPC uses its process model to simulate how the controlled variables will evolve over the prediction horizon—typically 20 to 120 control intervals into the future, depending on the process dynamics. This prediction is updated at every execution based on the most recent measurements, which allows the controller to continuously correct for the difference between model predictions and actual plant behavior.

The length of the prediction horizon is a design parameter. It must be long enough to capture the full dynamic response of the slowest-responding variable in the system. For a distillation column with a 60-minute composition response time, a prediction horizon shorter than three to four process time constants will cause the controller to make shortsighted decisions. Atlas Prediction Control reviews prediction horizon configuration as part of every performance assessment—it is a common source of suboptimal behavior in otherwise well-designed applications.

03

03

03

Optimization

Optimizing Control Actions

With a prediction of future process behavior in hand, the MPC solves an optimization problem at each execution to determine the sequence of control moves that minimizes a cost function—typically a weighted combination of deviations from setpoints across the controlled variables and the magnitude of changes in the manipulated variables—over the prediction horizon.

This optimization is where the real power of MPC resides. It simultaneously considers the effect of every manipulated variable on every controlled variable, accounts for the constraints, and finds the mathematically optimal set of moves. The controller then applies only the first move in that sequence. At the next execution, the calculation is repeated with updated measurements, a fresh prediction, and a new optimization solution.

The weights assigned to different controlled variables and manipulated variables in the cost function determine how the controller prioritizes competing objectives. Tuning these weights correctly—and keeping them aligned with current production priorities—is an ongoing engineering task. Atlas Prediction Control provides this optimization as part of its continuous improvement programs.

04

04

04

Constraint Management

Operating Within Process Constraints

The ability to explicitly handle constraints is one of MPC’s defining advantages. Engineers specify:

Hard constraints: Limits that must never be violated—equipment ratings, safety limits, environmental permit boundaries.

Soft constraints: Preferred operating ranges that the controller tries to respect but may relax if necessary to maintain feasibility.

Setpoint ranges: Ranges within which the controller has freedom to move a controlled variable in pursuit of an economic objective.

The solver handles these constraints mathematically, as inequalities in the optimization problem, rather than through external clipping or limiting logic. This means the controller naturally trades off competing constraints based on their assigned priorities—a capability that is essentially impossible to replicate with conventional regulatory control. Atlas Prediction Control helps manufacturers ensure these constraint definitions are accurate, current, and properly prioritized for their specific operational objectives.

05

05

05

Continuous Execution

Continuous Real-Time Adjustment

MPC executes on a defined control interval—which may range from seconds in fast chemical reactors to minutes in slower thermal processes. At each execution, it updates its predictions with fresh measurements, recalculates the optimal control action, and sends new setpoint targets to the underlying PID loops. This continuous receding-horizon execution means the controller adapts in real time to disturbances, feed variations, equipment changes, and shifting production targets.

In practice, the quality of this real-time adaptation depends entirely on how well the process model still represents the current process. As plant conditions evolve, that representation degrades—and with it, the quality of every prediction, optimization, and constraint management decision the controller makes. Keeping models current is one of the most important services Atlas Prediction Control provides to manufacturers with established MPC applications.

04

Types of Model Predictive Control Software

Not all MPC is the same. The appropriate type depends on the process characteristics, the degree of nonlinearity, the economic objectives, and the available engineering resources. Atlas Prediction Control helps manufacturers evaluate which architecture is appropriate for their specific application—and ensures that whatever type is deployed is configured and maintained to perform at its highest potential.

Industrial process environment

Process Architecture

Different processes require different control architectures.

Different processes require different control architectures.

Engineer reviewing technical equipment

Engineering

Large industrial facility

Industrial Scale

01

Most Widely Deployed

Linear MPC

Linear MPC is by far the most widely deployed form of model predictive control in industrial practice. It assumes that the process can be adequately described by a linear dynamic model—meaning that the relationship between inputs and outputs is approximately proportional and additive across the operating range of interest.

Strengths

Computationally efficient, well-understood, extensive industrial track record, supported by all major commercial platforms.

Limitations

Performance degrades in highly nonlinear processes or when the operating range spans regions where process gain and dynamics change significantly.

Typical Applications

Distillation columns, heat exchangers, fired heaters, compressor systems, blending operations, polymerization reactors operating within a defined grade range.

02

Wide Operating Ranges

Nonlinear MPC

Nonlinear MPC (NMPC) uses a first-principles or hybrid process model that explicitly represents the nonlinear behavior of the process. This allows the controller to maintain accuracy across wide operating ranges and through large transitions.

Strengths

Handles highly nonlinear processes accurately, performs better through large setpoint changes or grade transitions, can incorporate rigorous thermodynamic and kinetic models.

Limitations

Computationally demanding, requires significantly more engineering effort to develop and maintain, longer development timelines.

Typical Applications

Batch reactors, fluid catalytic cracking units, polymer grade transition control, processes with strong nonlinear interactions.

03

Model Evolution

Adaptive MPC

Adaptive MPC continuously updates its process model based on ongoing plant measurements, allowing it to track process changes over time without requiring a full model reidentification.

Strengths

Maintains accuracy as process conditions change, reduces the maintenance burden of model updates, well-suited to processes with measurable drift.

Limitations

Requires careful design to avoid model instability or overfitting to noise. Not appropriate in all process environments.

Typical Applications

Processes with gradual catalyst deactivation, polymer reactors with changing feed composition, processes subject to significant seasonal variation.

04

Economic Optimization

Economic MPC

Economic MPC (EMPC) replaces the traditional setpoint-tracking cost function with a direct economic objective—maximizing profit, minimizing operating cost, or maximizing production yield—subject to process constraints.

Strengths

Drives the process to the economically optimal operating point rather than a predefined setpoint, integrates real-time optimization (RTO) functionality directly into the controller.

Limitations

Requires accurate economic models and current pricing data; more complex to design, tune, and validate; may be difficult to explain to operations personnel.

Typical Applications

High-value continuous processes where the economic optimum is well-defined and the cost of suboptimal operation is significant.

05

Large Integrated Systems

Distributed MPC

Distributed MPC decomposes a large, complex plant into interconnected subsystems, each controlled by its own MPC that communicates with neighboring controllers. This is relevant in very large plants where a monolithic MPC application would be computationally intractable or organizationally impractical.

Strengths

Scalable to very large systems, subsystems can be maintained and updated independently, reduces computational burden.

Limitations

Coordination between subsystems adds complexity; performance may be suboptimal compared to a perfectly centralized solution.

Typical Applications

Integrated refinery units, large ethylene crackers, complex multi-unit chemical complexes.

01

Most Widely Deployed

Linear MPC

Linear MPC is by far the most widely deployed form of model predictive control in industrial practice. It assumes that the process can be adequately described by a linear dynamic model—meaning that the relationship between inputs and outputs is approximately proportional and additive across the operating range of interest.

Strengths

Computationally efficient, well-understood, extensive industrial track record, supported by all major commercial platforms.

Limitations

Performance degrades in highly nonlinear processes or when the operating range spans regions where process gain and dynamics change significantly.

Typical Applications

Distillation columns, heat exchangers, fired heaters, compressor systems, blending operations, polymerization reactors operating within a defined grade range.

02

Wide Operating Ranges

Nonlinear MPC

Nonlinear MPC (NMPC) uses a first-principles or hybrid process model that explicitly represents the nonlinear behavior of the process. This allows the controller to maintain accuracy across wide operating ranges and through large transitions.

Strengths

Handles highly nonlinear processes accurately, performs better through large setpoint changes or grade transitions, can incorporate rigorous thermodynamic and kinetic models.

Limitations

Computationally demanding, requires significantly more engineering effort to develop and maintain, longer development timelines.

Typical Applications

Batch reactors, fluid catalytic cracking units, polymer grade transition control, processes with strong nonlinear interactions.

03

Model Evolution

Adaptive MPC

Adaptive MPC continuously updates its process model based on ongoing plant measurements, allowing it to track process changes over time without requiring a full model reidentification.

Strengths

Maintains accuracy as process conditions change, reduces the maintenance burden of model updates, well-suited to processes with measurable drift.

Limitations

Requires careful design to avoid model instability or overfitting to noise. Not appropriate in all process environments.

Typical Applications

Processes with gradual catalyst deactivation, polymer reactors with changing feed composition, processes subject to significant seasonal variation.

04

Economic Optimization

Economic MPC

Economic MPC (EMPC) replaces the traditional setpoint-tracking cost function with a direct economic objective—maximizing profit, minimizing operating cost, or maximizing production yield—subject to process constraints.

Strengths

Drives the process to the economically optimal operating point rather than a predefined setpoint, integrates real-time optimization (RTO) functionality directly into the controller.

Limitations

Requires accurate economic models and current pricing data; more complex to design, tune, and validate; may be difficult to explain to operations personnel.

Typical Applications

High-value continuous processes where the economic optimum is well-defined and the cost of suboptimal operation is significant.

05

Large Integrated Systems

Distributed MPC

Distributed MPC decomposes a large, complex plant into interconnected subsystems, each controlled by its own MPC that communicates with neighboring controllers. This is relevant in very large plants where a monolithic MPC application would be computationally intractable or organizationally impractical.

Strengths

Scalable to very large systems, subsystems can be maintained and updated independently, reduces computational burden.

Limitations

Coordination between subsystems adds complexity; performance may be suboptimal compared to a perfectly centralized solution.

Typical Applications

Integrated refinery units, large ethylene crackers, complex multi-unit chemical complexes.

05

Key Features to Look for in MPC Software

Evaluating MPC platforms requires understanding which features matter for a given application—and which are table stakes versus genuine differentiators. Atlas Prediction Control helps manufacturers assess these features in the context of their specific process and operational requirements, ensuring that platform selection is driven by engineering reality rather than vendor presentations.

Capability only matters when it works in the plant.

Platform evaluation should move beyond feature lists. The real question is whether each capability can be configured, maintained, integrated, and improved throughout the full life of the application.

01

Process Fit

Does the platform match the complexity, speed, nonlinearity, constraints, and interactions of the process it will control?

02

Engineering Maintainability

Can the models, constraints, tuning, interfaces, and economic targets be maintained efficiently after commissioning?

03

Operational Usability

Can operators understand the controller, trust its actions, and recognize when performance begins to degrade?

04

Integration Reliability

Does the platform communicate reliably with the facility’s existing DCS, PLC, SCADA, data, and optimization infrastructure?

05

Long-Term Scalability

Can the implementation expand across additional units, products, operating modes, and business objectives without becoming fragile?

06

Measurable Value

Can performance improvements be verified against actual plant results rather than theoretical capability or vendor demonstrations?

06

Industries That Benefit from Model Predictive Control

MPC has proven valuable across a wide range of continuous and semi-continuous manufacturing industries. The common thread is the presence of multivariable interactions, tight product specifications, significant economic value at stake, and constraints that are difficult to manage manually or with conventional regulatory control. Atlas Prediction Control has deep application experience across each of these industries.

Industrial worker performing precision manufacturing work
Industrial worker performing precision manufacturing work

Process Environments

MPC creates value where complexity, constraints, and economics intersect.

Environments where MPC delivers its greatest value.

Each industry automatically opens as you move through the section. Scroll downward to advance and upward to revisit the previous environment. The panels remain clickable for direct navigation.

01

Polymerization Applications

Polyethylene Manufacturing

Polyethylene reactors are among the most natural applications for MPC. Melt index, density, production rate, and reactor stability are tightly coupled through complex nonlinear kinetics, making manual operation or simple PID control inadequate for maximizing throughput and minimizing grade transition time. MPC enables tighter product quality control, faster transitions between grades, and consistently higher operating rates. Atlas Prediction Control works with polyethylene manufacturers to implement, optimize, and continuously improve MPC applications that deliver measurable improvements in prime yield and production efficiency.

Prime Yield

Grade Transitions

Reactor Stability

02

Broader Polymer Production

Polymer Manufacturing

Across the broader polymer manufacturing sector—polypropylene, PVC, polystyrene, synthetic rubber—MPC addresses similar challenges: multivariable reactor control, product quality optimization, energy efficiency in finishing operations, and grade transition management. The economic value of faster transitions and higher first-pass prime yield is substantial in high-volume commodity polymer plants. Atlas Prediction Control helps polymer manufacturers capture this value through properly engineered and continuously optimized control applications.

Product Quality

Energy Efficiency

Transition Control

03

Complex Chemical Environments

Chemical Processing

Distillation, reaction, and separation processes in chemical manufacturing are classic MPC territory. The ability to simultaneously manage multiple product specifications, optimize energy use, and hold product purities tight to specification limits—while honoring column flooding limits, reboiler constraints, and feed variability—is difficult to achieve any other way. Atlas Prediction Control provides the process control engineering depth needed to implement and sustain high-performance MPC in complex chemical environments.

Distillation

Separation

Reaction Control

04

Integrated Process Operations

Petrochemical Operations

Ethylene crackers, aromatics plants, and integrated refinery-petrochemical complexes operate at the intersection of high throughput, tight product specifications, and significant energy intensity. MPC applications in these environments routinely deliver substantial annual improvements in margins through throughput optimization, energy reduction, and yield improvement. Atlas Prediction Control helps petrochemical operators maximize the return on their advanced process control investments across all major platforms.

Throughput

Margin Improvement

Energy Reduction

05

Continuous Food Production

Food & Beverage

Continuous food and beverage processes—evaporation, drying, pasteurization, fermentation—benefit from MPC’s ability to maintain consistent product quality despite variable raw material composition, ambient conditions, and demand. Energy optimization is particularly valuable in evaporator and dryer applications, where steam consumption is a major operating cost. Atlas Prediction Control helps food and beverage manufacturers implement control strategies that improve quality consistency and reduce energy intensity simultaneously.

Quality Consistency

Steam Reduction

Process Stability

06

Upstream and Downstream Operations

Oil & Gas

From crude distillation units and vacuum towers to gas processing, amine treating, and sulfur recovery, oil and gas operations represent one of the original—and still among the most productive—application domains for MPC. Throughput maximization against equipment constraints, product quality optimization across multiple streams, and energy efficiency are the primary value drivers. Atlas Prediction Control works with oil and gas operators to implement, recover, and continuously optimize MPC applications across the full range of upstream and downstream unit operations.

Constraint Control

Energy Efficiency

Product Optimization

Different industries. The same underlying signals.

MPC becomes most valuable when several operating challenges appear together. Atlas Prediction Control evaluates these signals to determine where advanced control can produce measurable value.

01

Interacting Process Variables

Interacting Process Variables

02

Tight Quality Specifications

03

03

Hard Operating Constraints

Hard Operating Constraints

04

High Energy Intensity

High Energy Intensity

05

Significant Economic Opportunity

Significant Economic Opportunity

05

Significant Economic Opportunity

Atlas Prediction Control

The software may be similar. The process never is.

Atlas Prediction Control helps manufacturers adapt MPC strategy, configuration, implementation, and continuous improvement to the realities of their specific operating environment. Whether a facility is evaluating MPC for the first time or trying to recover value from an established application, Atlas helps translate advanced control technology into measurable plant performance.

Atlas Prediction Control helps manufacturers adapt MPC strategy, configuration, implementation, and continuous improvement to the realities of their specific operating environment. Whether a facility is evaluating MPC for the first time or trying to recover value from an established application, Atlas helps translate advanced control technology into measurable plant performance.

07

Popular Model Predictive Control Software Platforms

The commercial MPC market is mature and well-served by a number of established platforms. The following are among the most widely deployed in industrial practice. This list is not exhaustive, and Atlas Prediction Control works across all major platforms—regardless of which one a manufacturer has chosen or is considering.

08

Common Challenges After Implementing MPC Software

The post-implementation phase is where the real work begins—and where many manufacturers discover that MPC performance is not self-sustaining. If any of the challenges below sound familiar, Atlas Prediction Control can help identify the root cause and restore the performance the system was designed to deliver.

Atlas Prediction Control

A declining MPC application does not always need replacement.

Atlas Prediction Control helps manufacturers separate symptoms from root causes, determine what has changed, and recover the performance their existing software and control infrastructure are capable of delivering. Whether the issue is model accuracy, plant changes, operator confidence, maintenance, or outdated objectives, Atlas can diagnose the gap and build a practical path back to reliable performance.

09

How Atlas Prediction Control Helps Manufacturers Maximize Their MPC Investment

Atlas Prediction Control is not a software vendor. The company does not sell, license, or distribute MPC software. Instead, Atlas Prediction Control provides the engineering expertise that determines whether a manufacturer’s investment in MPC software—whatever platform they have chosen—actually delivers the performance it is capable of.

Engineer working with industrial control and data infrastructure

The Atlas Difference

Software provides the capability. Engineering determines the result.

Six ways Atlas closes the gap between installed capability and actual performance.

Every stage addresses a different point in the MPC lifecycle—from understanding the current state of an application to rebuilding, recovering, and continuously improving its long-term performance.

ASSESS

Current-State Evaluation

Performance Assessment

Atlas Prediction Control evaluates the current state of an MPC application—model accuracy, constraint configuration, tuning, operator adoption, and operational performance—and identifies the specific gaps between current and achievable performance. This is the starting point for any engagement, whether the application was installed last year or a decade ago.

Model accuracy · Configuration · Tuning · Adoption

01

02

DEPLOY

First-Time Implementation

Implementation Support

For manufacturers selecting and deploying MPC for the first time, Atlas Prediction Control brings the process control engineering depth needed to execute identification campaigns, build accurate process models, configure and tune the controller, and commission the application correctly from the start. A properly implemented application from day one avoids the costly recovery work that poorly executed implementations require later.

Identification · Modeling · Configuration · Commissioning

REBUILD

Restore Model Accuracy

Model Rebuilding and Updates

When process changes or model drift have degraded controller accuracy, Atlas Prediction Control executes model reidentification campaigns, rebuilds affected model components, validates the new model against current plant behavior, and deploys the updated controller. This service is among the most commonly needed—and most impactful—in facilities with established MPC investments.

Reidentification · Validation · Updating · Deployment

03

04

OPTIMIZE

Continuous Engineering

Continuous Optimization

Ongoing engagement to review constraint limits, refine tuning, identify new optimization opportunities, and keep the application aligned with current production objectives and plant conditions. Atlas Prediction Control treats MPC performance as a continuous engineering discipline, not a one-time project.

Constraint review · Retuning · Opportunity identification

RECOVER

Restore Degraded Applications

Performance Recovery

For applications that have significantly degraded—or that were never properly commissioned—Atlas Prediction Control provides structured recovery programs that restore performance methodically and sustainably. Recovery engagements deliver rapid, measurable improvements in throughput, variability, and operator adoption.

Diagnosis · Recovery planning · Sustainable restoration

05

06

IMPROVE

Measurable Operational Results

Throughput and Variability Improvement

Using advanced statistical analysis, AI-driven process analytics, and deep process knowledge, Atlas Prediction Control identifies specific opportunities to push operating points closer to constraints, reduce variability, improve yields, and reduce operator intervention rates. These improvements are grounded in plant data and validated against operational results—not vendor benchmarks or theoretical projections.

Throughput · Variability · Yield · Operator intervention

Atlas Prediction Control

Already invested in MPC software but not getting the results you expected?

Atlas Prediction Control helps manufacturers close the gap between what their software is capable of and what it is actually delivering. Whether the application needs assessment, implementation support, model rebuilding, continuous optimization, performance recovery, or measurable operational improvement, Atlas provides the engineering expertise needed to maximize the investment.

10

Frequently Asked Questions About Model Predictive Control Software

Clear answers to common questions about MPC software, implementation, integration, and long-term performance.

Atlas Prediction Control

Still evaluating MPC—or questioning the performance of an existing system?

Atlas Prediction Control helps manufacturers determine where MPC creates genuine value, select and integrate the right approach, implement it correctly, and continuously improve the system after commissioning. Whether the plant is researching its first application or trying to recover an established one, Atlas can help identify the right next step.

11

Performance Assessment

Is Your Model Predictive

Control System Delivering

Its Full Potential?

Is Your Model Predictive

Control System Delivering

Its Full Potential?

Is Your Model Predictive
Control System Delivering
Its Full Potential?

Engineer evaluating industrial control system infrastructure

Existing MPC Performance Diagnostic

Existing MPC

Performance Diagnostic

Atlas Prediction Control

Recover performance. Find hidden value. Sustain the result.

ASSESS

Even the most advanced MPC platform loses effectiveness over time as operating conditions evolve, equipment changes, and production requirements shift. Process models drift. Constraints become outdated. Optimization opportunities go unrecognized. What was once a high-performing application becomes a source of operational frustration—not because the technology is flawed, but because sustaining MPC performance requires engineering expertise applied continuously, not just at commissioning.

Atlas Prediction Control helps manufacturers restore, optimize, and continuously improve Model Predictive Control performance through independent engineering expertise, AI-driven process analytics, and deep knowledge of the processes these systems are designed to control. Whether the objective is recovering performance from a degraded application, maximizing the value of a recent implementation, or evaluating whether an existing system is meeting its potential, Atlas Prediction Control provides the engineering partnership that translates software capability into measurable operational results.

But here is a reality that rarely appears in vendor brochures: purchasing and installing MPC software is only the beginning. The long-term performance of any MPC system depends almost entirely on the quality of its engineering, the accuracy of its underlying models, the rigor of its ongoing maintenance, and the expertise of the team responsible for keeping it optimized as plant conditions evolve.

Atlas Prediction Control works with manufacturers across the petrochemical, polymer, oil and gas, and specialty chemical industries to do exactly that. Whether a facility is evaluating its first MPC platform, recovering performance from a degraded application, or looking to extract more value from an investment already in place, Atlas Prediction Control provides the independent engineering expertise that translates software capability into measurable operational results. Not by selling software—but by ensuring the software a manufacturer already owns actually performs at the level it was designed to deliver.

Request a Performance Assessment

Request a Performance Assessment to discuss your existing control strategy, evaluate your current system performance, and identify where the greatest opportunities for improvement exist in your facility.

Request a Performance Assessment

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01

Independent Engineering Expertise

02

Platform-Agnostic Support

03

Performance Recovery and Optimization

04

Measurable Operational Results