Bioprocess design problems are frequently multivariate and complex. However, they may be visualised by a graphical representation of the design constraints and correlations governing both the process and system under consideration, namely windows of operation. Windows of operation exist at all stages of process design and find use both in the identification of key constraints from limited information, and also, with more detailed knowledge, the sensitivity of a process to design or operating changes. In this way windows of operation may be used to help understand and optimise a bioprocess design. In this paper the formulation, development and application of windows of operation is discussed for a range of biological processes including fermentation, protein recovery and biotransformation.

The use of windows of operation as a bioprocess design tool

Practical aspects of biofouling control in industrial water systems

Control strategies based on purely mathematical algorithms have only limited ability to cope with the type of operating conditions found in full-scale industrial applications. One approach to overcome this limitation integrates elements of automatic control theory, artificial intelligence and operations research into the design of a control system. This approach is employed in this research study to design an intelligent supervisory control system (ISCS). Elements of artificial intelligence to provide “human-like” characteristics for the ISCS are central to this research. Pseudo-Qualitative Modeling as a central component of an ISCS is proposed in this research. This approach provides the mechanisms required by the ISCS to handle heuristic knowledge and approximate reasoning required in many supervisory control applications. A simulation study has been performed to demonstrate the validity of this approach. Simulation results have shown that this technique can handle either poorly-defined heuristic models or accurate models based on mathematical concepts. In fact, pseudo-qualitative modeling provides a framework to integrate qualitative and numerical models into a knowledge-based system. A prototype of an ISCS was implemented using ProcessVision, a real-time SCADA (Supervisory Control And Data Acquisition) software package. This prototype system was applied to the C-line grinding circuit at Highland Valley Copper (HVC) to monitor and detect tonnage restrictions that affect circuit production. The diagnosis of tonnage restriction is currently performed manually by metallurgists on a weekly basis. This method is a heuristic procedure based on highly subjective judgment. The diagnosis results provided by the ISCS prototype during the evaluation period were well within the range of those reported by HVC metallurgists. Implementation of this ISCS prototype has demonstrated the feasibility of incorporating qualitative modeling into a commercial real-time SCADA system widely used in industrial applications.

A qualitative modeling facility for intelligent supervisory control systems (iscss)

A process is imitated using simulations as a quick way of its evaluation for further design and optimization purposes. Simulations, based on the availability of a computational mathematical model, are carried out with required assumptions for system inputs. Mathematical models for this purpose can be observation (empirical models depending upon the availability of experimental data with limited predictive capabilities) or physics based (via the formulation of the transport phenomena governing the process with outstanding predictive capabilities). The essential aspects of a physics-based mathematical model are to define physical, chemical, or biological changes, to develop a mathematical basis with appropriate assumptions, to solve a problem, and to validate for various situations. Model predictions can then be used for design and optimization purposes. In this chapter, mathematical modeling approaches to the simulation of food processing operations are summarized focusing on heat transfer and fluid dynamics. To this end, analytical solutions, numerical approaches, and computational fluid dynamics methodologies are illustrated, initial-boundary conditions and thermophysical properties are described, and an example of mathematical modeling of the thermal processing of canned foods is demonstrated. In addition, the significance of model validation and optimization is explained with traditional and innovative techniques.

Mathematical Modeling of Transport Phenomena for Simulation and Optimization of Food Processing Operations

Application d'un systeme de commande integre utilisant une structure hierarchique a plusieurs niveaux, aux problemes d'optimisation et d'information

J. A. Rogers

Papers

Optimizing process and economic aims