Isimite, Joseph Ogochukwu; (2024) Development of a Biorefinery Training Simulator with Automatic Operating Procedure Adaptation. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Biorefineries will increasingly play an important role in the decarbonization of energy supplies for transportation and other uses. Maintaining the talent pipeline of engineers and plant operators will be necessary for the safe and efficient operation of these complex biorefineries. Training simulators have been used in several sectors for the training and certification of technical competence. In this research, an improved training simulation with the functionality to automatically adapt operating procedures in response to process upsets was developed for a biorefinery. Mathematical process models were developed for the pre-treatment, liquefaction, and distillation unit operations of a large-scale biorefinery. Information provided by biorefinery operators and engineers was used to identify realistic training needs and to define technical and functional requirements for the training simulator. Model validation for the upstream units of the biorefinery was completed using process data from a biorefinery, while rigorous empirical and first-principles models were developed and validated for the distillation section by comparing modelling results with those from the literature. Operating procedures for the biorefinery were defined and automatic sequence control of the biorefinery was implemented using GRAFCET. This provided an effective means of comparing different operating scenarios including plant start-up, normal operations, and plant shutdown. To facilitate the useability of the training simulator, graphical user interfaces were developed based on emulated distributed control room (DCS) display screens, providing a look and feel similar to that of an actual biorefinery. To implement automatic operating procedure adaptation, an algorithm for process optimization, based on the Nelder-Mead simplex method for multi-dimensional optimization, was developed. The algorithm was integrated into the training simulator model, and the results of a base case operating scenario were compared with those obtained after process optimization of a plant that had experienced process upsets such as changes in enzyme activity, biomass viability, product inhibition, substrate inhibition, or fouling of critical heat exchange equipment. New optimum values for the actuating variables were found using the optimization algorithm and these provided the basis for automatic adjustment of recipe values to maintain plant operability and profitability. There was a 45% decrease in total operating costs for raw materials and energy for the optimized scenario compared to the base case scenario and increases of up to 20% in the total operating profits for the optimized cases compared to the base case. It is recommended that future research in this area should focus on increasing the complexity of the process models, for example, adding extensive heat integration and the effects of recycle streams will provide the capability to investigate more disturbances and the use of automatic operating procedure adaptation to compensate for such disturbances.

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