<![CDATA[The chemical industry is a cornerstone of the global economy, transforming raw materials into a vast array of essential products, from plastics and fertilizers to pharmaceuticals and fuels(Tickner et al., 2021). The efficiency, safety, and environmental sustainability of these transformations are paramount, not only for economic competitiveness but also for societal well-being. Central to achieving these goals is the discipline of process control—the engineering art and science of maintaining key process variables like temperature, pressure, flow, and composition at their desired values(Altmann, 2005). Without sophisticated control, chemical plants would be unstable, inefficient, and hazardous, incapable of meeting modern quality and regulatory standards. Historically, process control began with manual operator intervention and simple mechanical regulators, evolving through pneumatic and analog electronic systems to today's digital, computer-based Distributed Control Systems (DCS)(A˚ström & Kumar, 2014; Svrcek et al., 2014). This evolution has been driven by escalating demands. Economic pressures necessitate maximized throughput, minimized energy consumption, and reduced raw material. Simultaneously, stringent safety regulations and environmental protections require operations to remain within strict operational envelopes, preventing hazardous conditions and minimizing emissions. Furthermore, the modern market demands product quality with exceptionally low variability, a feat achievable only through precise and consistent automated control(Cusumano, 1992). Despite the proliferation of advanced algorithms, the Proportional-Integral-Derivative (PID) controller remains the fundamental building block, constituting over 90% of all control loops in a typical plant(Abbood et al., 2025). Its robustness and simplicity are unmatched for many single-loop applications. However, chemical processes are inherently complex—characterized by nonlinear dynamics, significant time delays, strong interactions between variables, and constraints on equipment and operations. These inherent complexities expose the limitations of conventional PID control when applied to optimizing entire unit operations or plants, creating a performance gap between base-layer stability and economic optimum(Mulholland, 2016). This gap is bridged by Advanced Process Control (APC). Since the 1980s, APC, particularly Model Predictive Control (MPC), has moved from an academic concept to an industrial staple. APC technologies use dynamic process models to predict future behavior and compute optimal control actions, explicitly handling multivariable interactions and operational constraints(Reitano, 2021). Their successful deployment on complex units like distillation columns, catalytic crackers, and polymerization reactors has demonstrated tangible benefits, including significant energy savings, yield improvements, and enhanced operational flexibility. Yet, the implementation and maintenance of APC require substantial investment and expertise. We now stand at the threshold of a new era fueled by the Fourth Industrial Revolution. The convergence of ubiquitous sensing, industrial Internet of Things (IIoT), massive data availability, and sophisticated data analytics like machine learning (ML) is reshaping the landscape. This digital transformation promises a new generation of hybrid and data-driven control strategies, adaptive systems, and autonomous operation(Nookala, 2024). Therefore, a comprehensive review that connects the enduring principles of conventional control, the established dominance of APC like MPC, and the emergent potential of digitalization is critically needed. Such a review provides a vital roadmap for practitioners and researchers to navigate the current state and future trajectory of process control in the chemical industry.]]>
