Decentralized proportional-integral-derivative (PID) control systems are widely used for multiple-input multiple-output (MIMO) control problems. However, decentralized controllers cannot suppress the plant interactions in multivariable systems, which are addressed in the controller tuning phase. In this paper, a decentralized PID tuning method is proposed in order to minimize the undesirable effects of the coupling between the inputs and outputs of the closed-loop system. For this purpose, the PID parameter tuning method solves a nonlinear optimization problem. This optimization problem is formulated with the criteria of the performance, robustness and multivariable stability of the closed-loop system. A single design parameter is required to specify the trade-off between performance and robustness. Simulation studies are conducted to demonstrate the effectiveness of the proposed method. The performance is compared to that of alternative tuning techniques from the literature. Results show that the proposed approach is a feasible candidate for industrial application, as it is simple to implement and capable of addressing robustness and stability concerns of plant operators.

Rotary drying is one of the most common unit operations in industries. It is a complex nonlinear process, and it is difficult to control with conventional methods. In this paper, a fuzzy multivariable controller is designed for an industrial rotary drying system. This fuzzy controller is applied to a fertilizer production unit to save energy and improve its performance. Based on the dryer operating conditions, the proposed control system aims to keep the exhaust gas temperature at the dryer outlet as close as possible to the optimum value. Thus, a reduction in energy consumption is achieved without losing the quality of the final product. Practical aspects of the industrial reality, such as the plant lacking an online moisture measurement instrument, are considered. The results prove that the proposed fuzzy control is able to reduce biomass consumption by 9%, which represents an annual reduction of 648 tons of carbon dioxide (CO ₂) emissions to the atmosphere.

This paper reports the calibration and validation of a cone crusher model using industrial data. Usually, there are three calibration parameters in the condensed breakage function; by contrast, in this work, every entry of the lower triangular breakage function matrix is considered a calibration parameter. The calibration problem is cast as an optimization problem based on the least squares method. The results show that the calibrated model is able to fit the validation datasets closely, as seen from the low values of the objective function. Another significant advantage of the proposed approach is that the model can be calibrated on data that are usually available from industrial operation; no additional laboratory tests are required. Calibration and validation tests on datasets collected from two different mines show that the calibrated model is a strong candidate for use in various dynamic simulation applications, such as control system design, equipment sizing, operator training, and optimization of crushing circuits.

Cascade control is widely used in the process industry, especially to reject disturbances. Typically, the controller parameters in the inner and outer loops of the cascade controller structure are defined in a strict sequence. In this paper, simultaneous tuning of cascade controllers is proposed to improve the performance of the system under load disturbance. For this purpose, the cost function of a nonlinear optimization problem is formulated to minimize the effect of disturbances on the system output. In addition, robustness constraints are defined for the maximum sensitivity function to independently guarantee a robust system for inner and outer loops. In this sense, the designer can explicitly and independently address the trade-off between performance and robustness for both loops. Simulation studies illustrate the performance and effectiveness of the proposed controller design method compared to well-known methods in the literature. These results indicate that the proposed method reduces the integrated absolute error (IAE) under load disturbance by up to [ ]. In addition, experimental results from a tank-level control application in the mineral process industry illustrate the practical applicability of the proposed method. In this case, the proposed method reduces the IAE under load disturbance by [ ].

Conveyor belts serve as the primary mode of ore transportation in mineral processing plants. Feeders, comprised of shorter conveyors, regulate the material flow from silos to longer conveyor belts by adjusting their velocity. This velocity manipulation is facilitated by automatic controllers that gauge the material weight on the conveyor using scales. However, due to positioning constraints of these scales, a notable delay ensues between measurement and the adjustment of the feeder speed. This dead time poses a significant challenge in control design, aiming to prevent oscillations in material levels on the conveyor belt. This paper contributes in two key areas: firstly, through a simulation-based comparison of various control techniques addressing this issue across diverse scenarios; secondly, by implementing the Smith predictor solution in an operational plant and contrasting its performance with that of a single PID controller. Evaluation spans both the transient flow rate during step change setpoints and a month-long assessment. The experimental results reveal a notable increase in production by 355 t/h and a substantial reduction in flow rate oscillations on the conveyor belt, evidenced by a 55% decrease in the standard deviation.