Learning to Control pH Processes at Multiple Time Scales: Performance Assessment in a Laboratory Plant
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S. Syafiie
This article presents a solution to pH control based on model-free learning control (MFLC). The MFLC technique is proposed because the algorithm gives a general solution for acid-base systems, yet is simple enough for implementation in existing control hardware. MFLC is based on reinforcement learning (RL), which is learning by direct interaction with the environment. The MFLC algorithm is model free and satisfying incremental control, input and output constraints. A novel solution of MFLC using multi-step actions (MSA) is presented: actions on multiple time scales consist of several identical primitive actions. This solves the problem of determining a suitable fixed time scale to select control actions so as to trade off accuracy in control against learning complexity. An application of MFLC to a pH process at laboratory scale is presented, showing that the proposed MFLC learns to control adequately the neutralization process, and maintain the process in the goal band. Also, the MFLC controller smoothly manipulates the control signal.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Review
- A Generalized Kinetic Model for Hydrometallurgical Processes
- Advanced Modeling of Reactive Separation Units with Structured Packings
- Applications of Computational Fluid Dynamics (CFD) Tools for Gravity Concentrators in Coal Preparation
- Article
- CFD Diagnosis of the Cyclotol Manufacturing Plant: Product Quality and Process Operation
- Performance Analysis of Three Controllers for the Polymerisation of Styrene in a Batch Reactor
- Enhanced Explicit Scheme to Solve Transient Heat Conduction Problem
- Analysis and Simulation of Cross-Flow Reactor for Ethylene Epoxidation
- Learning to Control pH Processes at Multiple Time Scales: Performance Assessment in a Laboratory Plant
- Estimating Output Variance in Closed Loop Systems
- CFD Simulations for Continuous Flow of Bubbles through Gas-Liquid Columns: Application of VOF Method
- Inference of Chemical Reaction Networks Using Hybrid S-system Models
- Optimization of the Variable Reflux Ratio in a Batch Distillation Column through a Heuristic Method
- Spatial and Temporal Resolution in Data-Driven Process Modeling of an Integrated Newsprint Mill
