Atikokan Digital Twin, Part B: Bayesian decision theory for process optimization in a biomass energy system. (15th March 2023)

• Record Type: Journal Article
• Authors: Spinti, Jennifer P.; Smith, Philip J.; Smith, Sean T.; Díaz-Ibarra, Oscar H.
• Is Part Of: Applied energy. Volume 334(2023)
• Publication Date: 2023-03-15
• Subjects: Bayesian decision theory -- Digital twin -- Artificial intelligence -- Uncertainty -- Optimization -- Biomass boiler; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.120625
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: We describe the integration of Bayesian decision theory in a digital twin framework to provide a process optimization tool for a biomass boiler. Our application is the Atikokan Generating Station, a 200 MW, biomass-fired tower boiler operated by Ontario Power Generation in Ontario, Canada. For this analysis, we use all available prior information as well as data from the biomass plant and from science-based models. Our objective is to determine a single-valued operational setpoint for the boiler that satisfies a set of objectives/constraints while accounting for uncertainty in boiler operations and in boiler measurements. This setpoint is then continuously updated at the frequency required by plant operations to provide dynamic control. This process of decision-making under uncertainty is a form of artificial intelligence and provides a formal methodology for making optimized decisions in complex systems. Our methodology consists of defining the decision space where all possible solutions reside, identifying the probability of outcomes given that a specific decision was made, creating a decision/cost model that relates the quantities of interest (QOIs) in the physical system (e.g. gross power output) to the decision QOIs (e.g. dollars), identifying the utility (the value to the user) of each outcome, and maximizing the expected utility (i.e. the decision). Once the decision (operational setpoint) is computed, we can predict all of the QOIs (boiler efficiency, O 2 concentration at the outlet, etc.) at the decision point from the science-based model using the parameter distributions computed as part of the Atikokan Digital Twin. Highlights: Digital twin of biomass boiler incorporates formal Bayesian decision theory to provide optimized operating-parameter set points which account for uncertainty. Cost model translates the physics quantity to the decision quantity (often cost). Utility maps the cost model to the value of the decision to the decision maker. Optimal decision results from maximizing the expected utility. Digital twin computes 790 outputs and their distributions at the optimal decision.