A Novel Intelligent Technique for Optimizing Chemical and Pharmaceutical Processes Based on Deep Optimized Reinforcement Model

In the recent times, optimization of chemical and pharmaceutical processes is critical for enhancing efficiency, yield, and sustainability. Conventional methods often rely on simplistic heuristics or computationally expensive models that fail to adapt to complex, dynamic production environments. To overcome this aforementioned problem, this research proposes a novel intelligent framework using Deep Optimized Reinforcement Learning (DRL), specifically a modified Markov Hidden Models (MHM) algorithm integrated with Dolphin Optimization, to intelligently control and optimize a continuous chemical operation process. The model is trained and validated on the Kaggle Chemical Operation data and performance mtetrics such as accuracy, precision, recall and F1-score are measured, achieving a system accuracy of 0.988 in operational accuracy and a 26.4 % improvement in product yield consistency with less latency. The novelty emerges from the algorithm's superior convergence stability and real-time adaptability, significantly outperforming traditional techniques, by improving operational cost and monitoring applications.