Expert Control Tuning of an Industrial Thickener

Bergh, L.; Ojeda, Pablo; Torres, Luis

doi:10.1016/j.ifacol.2015.10.083

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…The feed rate is only used in an emergency to avoid disturbances to plant production. The dependent variables include rake torque, underflow density, overflow turbidity, solids interface level (bed depth), solids inventory (bed pressure), solids settling rate and underflow viscosity [7].…”

Section: Process Description and Data Setmentioning

confidence: 99%

“…The data set used for data analysis contains data from both of Yara's paste thickeners, but after discussions with plant experts the data analysis was limited to Thickener 1. The whole data set contained altogether 73 measurements Figure 1: A typical thickener used in mineral processing [7] from February 2017 to February 2018. The frequency of measurements was one per minute.…”

Section: Process Description and Data Setmentioning

confidence: 99%

“…Remes, Aaltonen and Koivo [6] have developed a Kalman filter based soft sensor to monitor the thickener operation to reduce process disturbances. Bergh, Ojeda and Torres [7] have introduced the expert control tuning of an industrial thickener. Segovia, Concha and Sbarbaro [8] tested different thickener control strategies using a calibrated simulator, pointing out the weaknesses and giving hints for improving their performance.…”

Section: Introductionmentioning

confidence: 99%

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Data analysis of a paste thickener

et al. 2020

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AbstractThe solids content of slurry is typically increased in thickeners. A clean overflow and maximum solids concentration in the underflow are the general targets. The flocculant rate and underflow rate are the two independent variables that are typically used for control. The dependent variables include rake torque, underflow density, overflow turbidity, solids interface level (bed depth), solids inventory (bed pressure), solids settling rate and underflow viscosity. The research problem in question is that the outgoing paste is sometimes difficult to pump. The phenomena leading to this situation are not well known. In the worst-case scenario these phenomena cause clogging in the piping. A data analysis has been done to find the variables that affect and correlate with the pumping problem. The scope of this study covers the measurements from the feed line, thickener and underflow. The goal is to gain better understanding of the phenomena after this phase. The data analysis was done using the paste line pressure difference as a response variable and by dividing the data collected from Yara’s Siilinjärvi mill into two parts: operation areas with high and low pressure difference. The analysis is focused on Thickener 1 due to better availability of measurements. The knowledge of the variables found to influence the pressure difference can be utilized in further development.

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Section: Process Description and Data Setmentioning

confidence: 99%

Section: Process Description and Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data analysis of a paste thickener

et al. 2020

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“…Thickeners are usually connected to other equipment in mineral processing plants and wastewater treatment plants. [ 7 ] In order to minimize the equipment cost at a given water recovery rate, the hydrocyclone and thickener dehydration system structures were optimized. [ 8 ] The clarifiers and thickeners are also designed to have minimum solid residence times.…”

Section: Introductionmentioning

confidence: 99%

A two‐step coordinated optimization model for a dewatering process

Zhang

Wang

et al. 2020

Can J Chem Eng

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In actual production processes, the feed mass of a dewatering process is uncertain and a future production state cannot be predicted. This results in improper operation, a substandard production index, and a high energy economic index (EEI). To solve these problems, the authors propose a two-step coordinated optimization model for the dewatering process based on production data. The prediction model of the dewatering process is first established using the data accumulated during production. A two-step optimization model is then established to solve the problems existing in the dewatering process. The objective of the optimization is to minimize the EEI in the dewatering process, and the constraints are the ladder electricity price, operation safety, and production index. The genetic algorithm (GA) and gravitational search algorithm-genetic algorithm (GSA-GA) are used to solve the two-step coordinated optimization model, and the computational time can meet the application demand. An offline simulation and a field application showed that the optimization model can be used to improve the production index and reduce the EEI, loss due to the filter cloth, and the frequency of abnormal production.

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“…Therefore, expert controllers have been fruitfully applied to solve complex engineering problems, as well as for the control of industrial processes with time-varying parameters, nonlinearities, uncertainties, etc., see e.g., Refs. [66][67][68][69][70][71]. An important argument for applying expert controllers is the decrease of the effort required to effectively execute the control laws.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation of an Expert Model Predictive Controller in a Pilot-Scale Reverse Osmosis Plant for Brackish and Seawater Desalination

et al. 2019

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This article addresses the design and real-time implementation of an expert model predictive controller (Expert MPC) for the control of the brackish and seawater desalination process in a pilot-scale reverse osmosis (RO) plant. This pilot-scale plant is used in order to obtain the optimal operation conditions of the RO desalination process through the implementation of different control strategies, as well as in the training of operators in the new control and management technologies. A dynamical mathematical model of this plant has been developed based on the available field data and system identification procedures. Predictions of the obtained model were in good agreement with the available field data. The designed Expert MPC is distinguished by having a plant identification block and an expert system. The expert system, using a rule-based approach and the evolution of the plant variables, can modify the plant identification block, the plant prediction model, and/or the optimizer in order to improve the performance, robustness and operational safety of the overall control system. The real-time comparison results of the designed Expert MPC and a well-designed model predictive controller (MPC) show that the proposed Expert MPC has a significantly better performance and, therefore, higher accuracy and robustness.

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Expert Control Tuning of an Industrial Thickener

Cited by 12 publications

References 8 publications

Data analysis of a paste thickener

Data analysis of a paste thickener

A two‐step coordinated optimization model for a dewatering process

Real-Time Implementation of an Expert Model Predictive Controller in a Pilot-Scale Reverse Osmosis Plant for Brackish and Seawater Desalination

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