Optimizing abnormal operations of off-grid community utility systems with fuzzy P-graph

Chang

et al. 2022

MEQ

PurposeThis study is aimed to develop a novel intuitionistic fuzzy P-graph with Gaussian membership function to help decision-makers deal with complex process network systems.Design/methodology/approachTwo fuzzy P-graph case studies of the cogeneration system were selected, and relevant data were collected, including the structure and flow sequence of the system, and the rate of material and product transitions between the operating units. Gaussian function membership was set according to the restriction of fuzzy upper and lower bounds. Then the α-cut was used to obtain different upper and lower bound restrictions of each membership degree. After finding the optimal and suboptimal solutions for different membership degrees, the results of non-membership and hesitation were calculated.FindingsThe proposed method will help the decision maker consider the risk and provide more feasible solutions to choose the optimal and suboptimal solutions based on their own or through experience. The proposed model in this study has more flexibility in operation and decision making.Originality/valueThis study is the first to propose a novel intuitive fuzzy P-graph and demonstrates the effectiveness and flexibility of the method by two case studies of the cogeneration system. However, the addition of hesitation can increase the error tolerance of the system. Even for the solutions with a high degree of membership, optimal and suboptimal solutions still exist for the decision maker to select. Since decision makers expect the higher achievement of the target requirements; thus, it is important to have more feasible solutions with a high degree of membership.

Section: Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal synthesis of cogeneration systems using novel intuitionistic fuzzy P-graph

Chang

et al. 2022

MEQ

“…Because flexibility is an important element of an integrated renewable energy system, stochastic optimisation algorithms are one of the most commonly applied methods [69]. The Fuzzy -graph is another method that can be applied to optimise renewable energy utility systems, as used by Aviso [70] for the abnormal operation of an off-grid system. Various software tools for the planning of hybrid renewable energy systems, including HOMER, Calliope, RETScreen, DER-CAM, Compose, iHOGA, and EnergyPRO, were recently reviewed by Cuesta et al [71].…”

Section: Emerging Toolsmentioning

confidence: 99%

Emerging Tools for Energy System Design Increasing Economic and Environmental Sustainability

2020

Energy is a fundamental element supporting societal development, particularly with the increasing dependency on the Internet of Things. It is also the main contributor to environmental impacts and subsequently, a potential sector for mitigation. Sustainable energy system design considers energy savings and energy efficiency, waste and consumption reduction, process efficiency enhancement, waste heat recovery, and integration of renewable energy. Emerging tools range from advanced Process Integration, modelling, simulation, and optimisation, to system analysis and assessment. This review covers selected emerging studies promoting sustainable system design, including the recent developments reported in the Special Issue (SI) of the 22nd Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction (PRES’19). The primary emphasis was to enhance the economic and environmental performance. However, social factors were also highlighted as essential for future sustainable development. The discussion and analysis in this review focus on the most recent developments of (a) heat integration and heat transfer; (b) integrated and newly developed heat exchangers, (c) integration of renewables, and (d) roles in economic and environmental sustainability. The key results are highlighted, and future research ideas are suggested according to their links to a broader context.

“…P-Graph has also been well applied in the agricultural field. After Aviso et al blurred the drought level, a fuzzy P-Graph model about a micro-hydropower (MHP)- or wind-based system was constructed to find the optimal operation adjustment solution . They greatly expanded the application object field of P-Graph.…”

Section: Related Workmentioning

confidence: 99%

“…After Aviso et al blurred the drought level, a fuzzy P-Graph model about a micro-hydropower (MHP)-or wind-based system was constructed to find the optimal operation adjustment solution. 17 They greatly expanded the application object field of P-Graph. Pimentel et al studied the valorization of sugarcane with lactic acid as the main product target.…”

Section: Related Workmentioning

confidence: 99%

Feedstock Scheduling Optimization Based on Novel Extensible P-Graph Reasoning in Ethylene Production

Cao

Zhu

2020

Ind. Eng. Chem. Res.

Ethylene production plants have great differences in terms of equipment, technology, and yields. Moreover, the production cost and carbon emission are closely related to the solution of feedstock scheduling. Therefore, how to choose a reasonable solution of feedstock scheduling is a great challenge for decision makers. Traditional modeling and selection strategies are involved with a large number of parameters and data. Fortunately, the superstructure base on process graph (P-Graph) is friendly to decision makers in both modeling and optimization. However, when the required solution is missing from the case base, the superstructure needs to be constructed and optimized again, which will consume a lot of resources and costs. To solve the above problems, this paper proposes a novel reasoning strategy based on extensible P-Graph. In the proposed methodology, P-Graph is regarded as the basic method of superstructure optimization. In the absence of available solutions, a reasoning strategy is integrated into extensible P-Graph to obtain new feedstock scheduling solutions. To verify the effectiveness of the proposed methodology, three cases of missing structural schemes, missing parameters and structural solutions, and missing target features are carried out. Simulation results indicate that the proposed methodology can provide a good solution selection strategy with decision makers, and the final required solution can be found. In addition, the burden of modeling and optimization can be greatly reduced.