MPC vs Expert Systems How They Transform Mineral Processing Automation

Mineral processing operations — and grinding circuits in particular — are notoriously challenging environments for automatic process control. Unmeasured ore property varia tions, material transport delays, and nonlinear response characteristics make life difficult for any controller. So, what are the industry’s real options? And how are operators, process engineers, and vendors tackling these complexities today? In this blog, we explore two leading approaches: Model Predictive Control (MPC) and Expert Systems, contrasting their foundations, strengths, and real-world impacts, with insights and references from industry surveys and technical papers.

MPC is a sophisticated control strategy used to optimize the performance of dynamic systems. Unlike traditional control methods, which react to current conditions, MPC anticipates future behavior by using a model of the system. At each time step, it solves an optimization problem to determine the best control actions, considering constraints and balancing trade-offs. This predictive approach allows MPC to handle multiple interacting inputs and outputs, reject disturbances, and operate efficiently even under tight constraints.

Expert systems are a branch of artificial intelligence (AI) designed to simulate the expertise and decision-making of human specialists by using encoded knowledge and logical rules. They solve complex problems by using “if-then” rules to mimic how an expert would think, instead of following a fixed set of step-by-step instructions like most traditional computer programs. Expert systems can be categorized based on their structure into: Rule-Based, Frame-Based, Fuzzy Logic, Neural Network-Based, and Neuro-Fuzzy expert systems.

In process control particularly controlling a mine’s grinding or flotation circuit, Model Predictive Control has emerged as another leading technique. It’s natural to compare expert systems with MPC, as both aim to automate and optimize control– but they do so in very different ways.

Control Strategy: An expert system uses a knowledge-driven approach. It imitates the decision process of human experts through predefined rules encoded in software. On the other hand, MPC is a model-driven, predictive approach. It uses a mathematical model of the process (often a state-space or empirical model derived from system identi f ication) to predict future outputs. At each control interval, MPC finds the sequence of control moves through optimizing some objective (maximize throughput, minimize error, etc.) over a future horizon, while respecting constraints (such as equipment limits).

Adaptability and Learning: Classic expert systems do not automatically adapt as they require new rules to handle new situations. They also tend to respond poorly to process upsets or new disturbance patterns. MPC, in contrast, can be more adaptive within the bounds of its model. MPC will autonomously adjust the control moves to compensate the disturbances captured by the model, since it continuously re-solves the optimization with the latest data. Modern MPC implementations sometimes incorporate machine learning to update models or handle non-linearities, giving them more robust ness in nonlinear systems such as grinding circuits.

Optimality: MPC can optimize performance by balancing trade-offs (such as increasing throughput while controlling product quality and respecting equipment limits). This often leads to more optimal solutions in a fine-tuned manner than a rule-based method. Essentially, MPC can often drive closer to constraints safely because it explicitly predicts future violations, whereas expert system rules are usually conservative or approximate to avoid trouble. A key strength of MPC is its ability to optimize the entire system as a whole, not just individual parts. By modeling the whole grinding circuit together, it then predicts how adjustments in one part (say, feeder speed or water flow) will affect other parts (like mill load or product particle size). Finally, it coordinates all these variables together to find the global optimal operating point. On the contrary, expert systems such as fuzzy logic control typically optimize locally, focusing on one piece of equipment or one variable at a time, rather than performing a global optimization of the whole circuit. This difference is why MPC often yields superior results in throughput and efficiency when managing complex processes like grinding circuits.

 Multivariable Handling: MPC excels at handling multivariable control problems with constraints. For example, a grinding circuit with multiple mills, pumps, densities, etc., where actions on one variable affect others, can be coordinated by a single MPC controller that considers all interactions. An expert system could handle multivariable situations too, but it would require an exponentially growing rule set to cover all combi nations, which becomes impractical.

 Bottom line, Model Predictive Control (MPC) consistently keeps grinding circuits running close to their best, reducing variability and boosting throughput. As operations become more complex, more plants are turning to MPC for its reliability and robust performance. While expert systems have played a key role, MPC is now the preferred choice for achieving stable, efficient results in today’s demanding industrial environments. Specifically in grinding, reports show the use of MPC resulted in increased mill through put—on the order of 1.5–2% higher production in SAG mills compared to expert system control alone. These improvements are significant in mining, equating to millions of dol lars per year in added profit.

 Now one might ask if expert systems are obsolete? The answer is ”not at all”. Instead, we are seeing a fusion of approaches. The future lies in hybrid architectures: systems that combine the strengths of expert reasoning and MPC which helps transition from reactive to predictive decision-making while balancing efficiency and risk.