Why are engineers interested in optimization? What benefits result from using this method rather than making decisions intuitively? Typical problems in chemical engineering process design or plant operation have many solutions. Optimization is concerned with selecting the best among the entire set by efficient quantitative methods. This article is commenting some of them.

Although most process and control engineers are mostly fascinated by technological challenges that drew them into a technical career,  it should never be forgotten that these processes are business processes operating in a competitive global marketplace. A process will remain operational within a commercial enterprise so long as its performance is economically competitive and is contributing to the financial health of the company. So, the wonder is: What is the business process viewpoint for performance monitoring, performance assessment and performance improvement?

The integrated control and process optimization approach is a means for identifying areas and bottlenecks where advanced control and optimization technology can have an effect on the process revenue. 
Prior to any benchmarking analysis or changes in control, detailed plant auditing involving process, control and management objectives should be carried out. 

These 5 steps are describing the way towards process optimization:

The distillation process of propane-propylene, one of a difficult separation processes and a major consumer of energy, has been improved in practice on commercial plant by advanced process control including experimental plant tests, mathematical model identification and multivariable controller design and implementation. 

Model predictive control algorithm is the heart of traditional advanced process control applications. It has been the decades now of its continuing industry implementations, bringing millions of dollars benefits to industries all over the world. 

The model-based control strategy that has been most widely applied in the process industries is model predictive control (MPC). It is a general method that is especially well suited for difficult multi-input, multi-output (MIMO) control problems where there are significant interactions between the manipulated inputs and the controlled outputs.