9JUNE 2024VARIABILITY IS THE VILLIANOBSERVATIONS OF OPERATIONS MANAGEMENT OF MINERAL PROCESSING MILLSBoth have a tremendous amount in common. Both focus on a culture of continuous improvement, root cause analysis, elimination of inefficiencies, and a lifetime of assiduous commitment to the cause (not a fad). For a good comparison, read "Theory of Constraints and Lean Manufacturing: Friends or Foes?" by Moore and Scheinkopf, 1998. Process ControlAt the same time that operating philosophies were developing, circa 1985, researchers and practitioners in the process control world were exploring the interaction of fuzzy set theory and expert systems in a construct called artificial intelligence. At that time, AI had the meaning of mathematical modeling and computer simulation of decisions that replicated people who were experts in their roles. A good summary of that journey is provided by Gaines and Shaw from the Department of Computer Science at York University. The article provides the principles of Expert System process control:1. thoroughly instrument the system to be controlled or about which decisions are to be made2. use the instrumentation to gather data about the system's behavior under a wide variety of circumstances3. from these data, build a model of the system that accounts for this behaviour4. from this model, derive algorithms for decision or control that are optimal in terms of prescribed performance parametersIn the thirty-to-forty-year period since, technological improvements in sensors, computing, and human-machine interfaces have resulted in step changes in Expert Control System capability. Combining operating philosophies and technology in operations has seen profound changes in the manufacturing industry worldwide. Generically, manufacturing systems are explicit, mechanistic, and controlled in real-time. However, some industries find it difficult to maintain adherence to the fundamental principles of an expert control system. This is especially the case in situations where plants are large, complex, or uncertain, or there may appear severe changes in operating conditions. We have just described the mineral processing circuit.Monov, Sokolov, and Stoenchev talk about this in their paper "Grinding in Ball Mills: Modelling and Process Control," 2012 (a decade ago). They say, "The process control in a ball mill grinding circuit faces severe difficulties due to the following well-known characteristics: the process is nonlinear with immeasurable disturbances and unmodelled dynamics; there are strong interconnections among variables so that each input variable interacts with multiple output variables; the time constants of the process have values in a wide range, and there are significant time delays in some input-output pairs; the system model contains a number of integrators; the process parameters vary in time as the circuit ages; there are technological constraints on the manipulated and controlled variables; the measurements are unreliable and noisy." Variability is the insurmountable villain.The FutureUsing Moore's Law of computing power as a rule of thumb, it is inevitable that AI will close the variability challenge gap in process control of tumbling mills. Supporting the computing power will be technologically advanced sensors that are robust, reliable, and repeatable in their data streams. At the same time, philosophies like LEAN and TOC will result in a demand for explicitisation and mechanistic processes, including a war on waste, that erode the variability of the process. Measure, measure, measure. Consistently, assiduously, and without variability.The mineral processing teams need only to look at best-practice manufacturing facilities to see where the future lies. Using Moore's Law of computing power as a rule of thumb, it is inevitable that AI will close the variability challenge gap in process control of tumbling mills
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