Related Developments

Increased use of batch processes in various industries has invigorated research and development in batch process design and operation. Recent developments in batch process modeling, monitoring, diagnosis, and control have been presented in earlier chapters of the book. Related developments in other aspects of batch processes are presented in this chapter, focusing on three areas:

• Process development and modeling using metabolic engineering and pathway analysis

• Dynamic optimization of batch process operations

• Integration of monitoring, control, and diagnosis activities using supervisory systems

The earlier chapters of this book presented many powerful techniques that have been developed for batch operations or extended from other fields to improve modeling, monitoring, diagnosis, and control of multivariable fermentation processes. This chapter introduces additional research directions and techniques that will have an impact on bioprocess operations, and in particular the operation of multivariable batch fermentation processes.

Advances in process development have been influenced by better understanding of fundamentals of fermentation processes and developments in metabolic engineering, focusing on metabolic pathway analysis and modification. The role of metabolic engineering in process improvement is discussed in Section 9.1.

Progress in process modeling can be discussed based on advances in various key areas. One influential factor is the interest in building layers of models, starting with the model of a cell. An ambitious plan in biomedical applications is to integrate the models of cells to build models of organs and integrate the models of organs to build models of the body for conducting computational experiments. The availability of such models for screening potential drug candidates in pharmaceutical industry will have significant impact on drug development time and cost. Advances in fundamental areas such as biology, biochemistry, mathematics, computer science and bioengineering have contributed to progress in the development of these multi-layer first principles models. Methods for developing first principles models of batch fermentation processes were introduced in Chapter 2. Information on metabolic pathways and integration of systems science methods and metabolic pathway analysis can provide the tools to add detailed knowledge to first principles models. Metabolic flux and control analysis are introduced in Section 9.2 to underline the use of sensitivity analysis in model development. The other alternative for describing a batch fermentation process is an empirical model discussed in Chapter 4. The existence of many nonlinearities in living systems has motivated researchers for developing nonlinear empirical models. Advances in statistics, computer science, mathematics, and systems science enabled development and application of nonlinear model development techniques in many fields. Section 4.7 presented extensions of linear model development techniques and Chapter 5 introduces many useful techniques for modeling and analyzing the dynamic behavior of nonlinear systems.

Progress in dynamic optimization of batch fermentation process operations is influenced by advances in modeling, optimization, statistical methods, and control theory. Model predictive control (MPC) presented in Section 7.6 relies on similar techniques and focuses on tracking a reference trajectory while rejecting the effects of disturbances. The performance of MPC systems is strongly related to availability of good process and disturbance models, and powerful optimization techniques. Dynamic optimization methods offer a variety of alternatives to select optimal values of process inputs and switching times to maximize productivity and yield. The alternatives in dynamic optimization of batch processes, current practice and emerging technologies are discussed in Section 9.3.

Software environments for efficient real-time operations and powerful computer hardware enable horizontal and vertical integration of various tasks in guiding batch process operations. Horizontal integration focuses on the coordination of monitoring, diagnosis, and control tasks. Vertical integration focuses on the coordination of process operations related tasks with higher level management tasks. Section 9.4 presents supervisory knowledge-based systems (KBS) to implement horizontal integration and introduces vertical integration paths with supply chain management and plantwide optimization.

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