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CHEM Résumé de rapport

Project ID: G1RD-CT-2001-00466
Financé au titre de: FP5-GROWTH
Pays: Spain

AI-based optimisation strategies

The aim of the CHEM project was to develop and implement advanced Decision Support Systems (DSS) for process monitoring, data and event analysis, and operation support in industrial processes. The systems are synergistic integration of innovative software tools, which improve the safety, product quality and operation reliability as well as reduce the economic losses due to faulty states, mainly in refining, chemical and petrochemical processes.

The CHEM applications consist of integrated sets of software toolboxes that provide robust detection and diagnosis of process problems in real-time. The systems assist operators in assessing process status and responding to abnormal events. The project provides a flexible architecture and a methodology in order to facilitate the development of such applications on many processes.

The approach proposed in 'Scheduling and planning procedure under multiobjective criteria' toolbox is a practical one since the efforts are dedicated to help the user to explore, analyse and find a feasible solution better than any other they could expect within the limited period of time for the decision-making. However, the solution reached is not strictly the optimum in mathematical terms. This will arise from the rigorous solution of the global optimisation problem, which is an unaffordable task to attempt without a very good starting point.

General purpose models, based on MILP and MINLP formulations, run out of proportion when trying to solve complex scheduling problems. Different algorithms (based on MILP formulations) have been reported for the optimal scheduling of multiproduct/ multipurpose batch plants. This toolbox will provide a new graph theoretical approach to efficiently solve the scheduling of multiproduct/multipurpose batch plants with intermediate storage. Specifically, a graph theoretical approach for solving multipurpose batch plants is applied.

This representation has the advantage of exploiting the problem-specific knowledge from the very beginning to develop efficient algorithms. Therefore, the optimisation strategy appears as a symbiotic procedure that will take advantage of practical approach ('Scheduling and planning procedure under multiobjective criteria' toolbox) to feasible good solutions to build-up automatically good starting points for the rigorous solution of the problem. Hence, the graph-theoretic approach uses a practical good starting point to search for optimal solutions. Thus after the activation energy initially given by the user, the Optimisation Module will go readily towards the optimum.


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