摘要：The high complexity of integrated processing plants makes it a hard problem for managers and operators to find the best operational strategy. And it becomes even more difficult when they have to deal with more than one criterion for optimality because trade-offs between conflicting goals have to be taken into account. Usually optimisation problems are set-up with a single objective function, where several criteria are compressed into one figure by weighting factors. Thus, the result is a single number without any leeway in decision making. In contrast, multi-criterial optimisation reveals the room for manoeuvre. Since the plant personnel have to balance several requirements in order to run the plant in an “optimal” fashion, we propose to use multi-criterial optimisation to assist them in their daily decisions. A prototypical tool was developed and the approach is applied to a real-world problem; a Butadiene plant in combination with cooling towers. The Butadiene plant consists of distillation columns and consumes a solvent, heating steam and cooling water, the cooling towers consume electric power. Thus, the criteria for the optimisation are the minimisation of these utilities. As they are interchangeable to some extent, conflicting goals appear naturally and the multi-criterial optimisation reveals the important interdependencies.
关键词：Decision Support SystemsMultiobjective OptimisationGenetic AlgorithmsChemical Industry
