Big Models using Big Data for Simulation-based Design and Operational Optimization

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Simulation-based engineering science is a rapidly growing domain where numerical simulation of complex models are used to make design decisions. However, for the models to represent reality, it is better that they be based on collected data. On the other hand, an emerging area of research activity is the Big Data analytic that uses large volume of data in real-time from a variety of information sources for decision-making. Also, new developments in the modeling field relates to network systems, where a number of sub-models are connected with a given topology. This proposal combines all the above domains, by constructing Big Models from Big Data, which will then be used for simulation-based design and optimal operation.******Big Models are clusters of different types of sub-models, derived from Big Data, to represent a given system. Big Models inherit the four Vs of Big Data. The volume aspect of Big Models corresponds to the number of variables used, the variety aspect to the different types of sub-models that co-exist, the velocity aspect to the dynamic or time-varying nature, and the variability aspect to the model uncertainty. It is noted that the construction of Big Models requires a fractured approach in the beginning, followed by an eventual integration.******The proposal proposes a systematic methodology for the construction of such a network model. It also develops algorithms for regression with Big Data. The proposal continues with the two aspects of Process Systems Engineering, i.e., the design and operation. Design decisions are made either using a sensitivity analysis, a mixed-integer programing along with a heuristic simplification. Safety and flexibility are taken into consideration. Operational decisions are taken based on a combination of numerical optimization and real-time Big Data. For the latter, a multi-unit approach is proposed, where a Big Model runs in parallel to the system and the difference between data simulated by the Big Model and the obtained Big Data is used for adaptation.******The proposed methodology will be applied to two case-studies, (i) the aircraft environmental control and (ii) waste water treatment using microbial fuel cells. Both of them have heterogeneous subsystems, and each of the sub-systems has a completely different modeling methodology. The data are also very varied, from direct to indirect measurements. The design aspect will be tested on the aircraft problem, while operational optimization will be carried out on the waste water treatment case.