Advancements in Intelligent Chemical Process Control
*Corresponding Author:Received Date: May 01, 2025 / Accepted Date: May 29, 2025 / Published Date: May 29, 2025
Citation: Khan A (2025) Advancements in Intelligent Chemical Process Control. Ind Chem 11: 340.DOI: 10.4172/2469-9764.1000340
Copyright: © 2025 Aisha Khan This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Abstract
This compilation of studies addresses key challenges in chemical process control and optimization. It covers advanced control
strategies like Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL), alongside data-driven fault detection and
diagnosis. The research also delves into robust control for uncertain systems, digital twin integration for real-time optimization, and
adaptive fuzzy logic control for precise temperature regulation. Furthermore, multi-objective optimization, safety instrumented sys
tems, parsimonious model identification, and statistical process control for quality improvement are explored. These advancements
collectively aim to enhance efficiency, safety, and sustainability in chemical manufacturing.
