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The objective of this paper is to give a critical survey of the present status within the field of control of a reactive distillation column. Control of a reactive distillation column is a challenging task due to process nonlinearity and complex interactions between the vapor-liquid equilibrium and chemical reactions. There are different types of control methodologies, which have been studied in the reactive distillation, ranging from a simple proportional-integral (PI) controller to advanced model predictive controllers (MPC) such as dynamic matrix control (DMC), quadratic dynamic matrix control (QDMC), robust multivariable predictive control technology (RMPCT), generalized predictive control (GPC), and other advanced control techniques. With the goals of optimal performance, energy conservation and cost effectiveness of process operations in industries, the design of optimal controllers and controller performance assessment have received great attention in both industries and academia. The main objective of control is to maintain the product purity within the desired range.

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The concept of Intelligent Databases was introduced in 1980's, as expectations from the conventional databases had increased over the time. A database which could manage information on its own was much anticipated. Over the years, there has been significant development and advances in this field. Different approaches of integrating AI/DB to form an Intelligent Database have been introduced from time to time. The underlying concepts that have been proved separately as well as jointly, are the prime motivations and foundations of this paper. In addition to this, the use of artificial intelligence for prediction/forecasting has led to the conceptual approach of putting together more than two technologies for the betterment of not only our future, but for the next generation of computing. Few different approaches to AI/DB integration have been studied and reported in this paper.