Design and analysis for efficient simulation in petrochemical industry

Abstract

Building an industrial simulation model is a very time and cost intensive exercise because these models are large and consist of complicated computer code. Fully understanding the relationships between the inputs and the outputs are not straight forward and therefore utilizing these models only for ad hoc scenario testing would not be cost effective. The methodology of Design and Analysis of Simulation Experiments (DASE) are proposed to explore the design space and pro - actively search for optimization opportunities. The system is represented by the simulation model and the aim is to conduct experiments on the simulation model. The surrogate models (metamodels) are then used in lieu of the original simulation code; facilitating the exploration of the design space, optimization, and reliability analysis. To explore the methodology of DASE, different designs and approximation models from DASE as well as the Design and Analysis of Computer Experiments (DACE) literature, was evaluated for modeling the overall availability of a chemical reactor plant as a factor of a number of process variables. Both mean square error and maximum absolute error criteria were used to compare different design by model combinations. Response surface models and kriging models are evaluated as approximation models. The best design by model combination was found to be the Plackett - Burman Design (Screening Phase), Fractional Factorial Design (Interaction Phase) and the Response Surface Model (Approximation Model). Although this result might be specific to this case study, it is provided as a general recommendation for the design and analysis of simulation experiments in industry. In addition, the response surface model was used to explore the design space of the case study, and to evaluate the risks in the design decisions. The significant factors on plant availability were identified for future pilot plant optimization studies. An optimum operating region was obtained in the design variables for maximum plant availability. Future research topics are proposed.