Fuzzy control of a neutralization process

Proof-of-Concept Design of an In-Line pH Neutralization System with Coarse and Fine Adjustments for the Continuous Manufacturing of Pharmaceuticals

“…Generally, in this setup, a conventional proportional-integral (PI) or proportional-integral-derivative (PID) controller is not sufficient to achieve the desirable adjustment goals. This is because of the inherent nonlinearity of the process and its sensitivity to small disturbances in an unbuffered system, especially when the adjustment set point is centered around an equivalent point . To overcome such control problems, process models and sophisticated algorithms have been developed.…”

“…To overcome such control problems, process models and sophisticated algorithms have been developed. Examples include genetic adaptive PI control using internal model control (IMC), adaptive nonlinear feedback control, model predictive control (MPC) based on a dynamic matrix (DMC), model-free learning control (MFLC), Wiener and Hammerstein models, strong acid equivalent control, fuzzy control, ,, neural networks, and hybrid models that integrate multiple control strategies. − Although these proposed models have demonstrated an excellent ability to precisely control pH, most of them have only been examined via simulation. They do not account for potential disturbances and other technical difficulties associated with experimental and physical industrial implementation in real-time manufacturing processes ( e.g ., nonuniformity of the feed stream, flow rate disturbance, and variations in equipment performance).…”

“…This is because of the inherent nonlinearity of the process and its sensitivity to small disturbances in an unbuffered system, especially when the adjustment set point is centered around an equivalent point. 17 To overcome such control problems, process models and sophisticated algorithms have been developed. Examples include genetic adaptive PI control using internal model control (IMC), 18 adaptive nonlinear feedback control, 19 model predictive control (MPC) based on a dynamic matrix (DMC), 20 model-free learning control (MFLC), 21 Wiener and Hammerstein models, 22 strong acid equivalent control, 23 fuzzy control, 17,24,25 neural networks, 26 and hybrid models that integrate multiple control strategies.…”

“…17 To overcome such control problems, process models and sophisticated algorithms have been developed. Examples include genetic adaptive PI control using internal model control (IMC), 18 adaptive nonlinear feedback control, 19 model predictive control (MPC) based on a dynamic matrix (DMC), 20 model-free learning control (MFLC), 21 Wiener and Hammerstein models, 22 strong acid equivalent control, 23 fuzzy control, 17,24,25 neural networks, 26 and hybrid models that integrate multiple control strategies. multiple tank reactors in series to eliminate nonlinear characteristics.…”

Optimization-Based Input/Output Linearizing Control Strategy for a pH Process with Multiple Titrant Streams

“…Galán et al , applied the multilinear model-based H ∞ control to handle a wide range of pH in the neutralization process. A fuzzy logic controller, in which pH characteristic was divided into multiple fuzzy regions, has been proposed . An adaptive input–output (I/O) linearizing controller with a recursive least-squares estimation in the process buffer capacity has been developed .…”