Enhancing Computational Performances in Chemical Processes Costs and Emissions Prediction: a Surrogate Modelling Based Approach

Abstract

The increasing amount of variables to be accounted for in chemical processes optimization and the need to have a systemic approach to include all the steps of the industrial production chain implies the exponential growth of the model equations to be solved at the same time. In fact, in order to have an optimal industrial system, the analysis should start from raw materials supply and include demand-side, process side and logistic from the meso- to the macro-scale perspective. Moreover, beside economics, environmental impact, flexibility and scheduling should be coupled in a multi-objective optimization loop. This approach results in a computational effort that is way higher than that required in the past for conventional process optimal design. Therefore, innovative computational strategies should be implemented in order to ease the optimization loop. During the last decade, surrogate modelling has seen renewed interest for this purpose in chemical process engineering and it has been widely used for feasibility analysis, optimization and optimal scheduling. In this preliminary study we exploit a surrogate modelling approach for costs and emissions calculation for a simple separation process. A distillation unit is simulated by means of ProSimPlus process simulator to retrieve a set of physical and economic data over the operating domain of interest. After that, the sampling strategy is selected according to the suggested standards and adopted to generate a surrogate modelling with a Response Surface Methodology approach by means of ALAMO software. The output variable of interest for this study have been identified as the unit costs and the emissions related to the energy consumption. Despite the complexity of chemical equilibrium in multistage units, the obtained results show good agreement with those generated by the phenomenological models with a computational time whose magnitude is two orders lower. In conclusion, this methodology is worth deeper studies in order to be exploited for more complex systems and have even more benefits with the increasing complexity of the case study when coupling more units in different configurations.