Optimal Design and Scheduling of Offshore Oil and Gas Field Development with Environmental Impact Consideration

The objective of the paper is to develop a mixed integer nonlinear programming (MINLP) model for optimum design and scheduling of offshore oil and gas field development in respect to simultaneous consideration of economic and environmental impact. The model is utilized as a tool for decision making management in conceptual stage. Nonlinear reservoir behavior and floating demand constraint are incorporated to improve accuracy of the solution. This paper utilizes mathematical programming techniques to address the design and scheduling problem of offshore oil and gas field development. Field development problem is first formulated into a multi-objective MINLP model incorporating many realistic features such as nonlinear reservoir behavior and floating demands. The objectives are to maximize net present value (NPV) and minimize total environmental impact (TEI) simultaneously. Environmental impact is assessed using the ReCiPe2016 method. Augmented ε-constraint method (AUGMECON) is then employed to solve the proposed multi-objective MINLP model to generate the Pareto-optimal front that is able to assist decision maker selecting the most preferred solution. The performance of the proposed modelling framework is investigated on a set of problem which consists of 2 reservoirs, 2 FPSOs, 2 customers and 5-years planning horizon. First model with single objective function to maximize NPV can be solved effectively within short computational time. The solution gives optimum decision of design, investment, production schedule, and transportation regardless the environmental impact. Then, simultaneous optimization of multi-objective MINLP with different value of ε-constraint generates multiple development schemes and objective function values. The results indicate trade-off between maximizing NPV and minimizing TEI. It is possible to obtain maximum NPV of USD 2.4 trillion at the expense of TEI which is 307.518 or to generate minimum TEI of 16.65 at the expense of NPV which is USD 74.368 billion. All possible solutions within extreme values range are presented in form of a Pareto-optimal front where TEI and NPV are plotted in x and y-axis respectively. It will assist the company to select the most preferred solution based on NPV. Consequently, the selected option brings corresponding value of TEI. Additionally, the Pareto optimal front also allows decision maker to have more flexibility to compromise between economic and environmental issues. This is the first study to consider environmental impact in the offshore oil and gas field development. Many realistic operational features such as nonlinear reservoir behavior and floating demands are also incorporated. In addition to that, the proposed framework yields a powerful tool to assist decision maker selecting the most preferred solution that satisfies their criteria in both economic and environmental aspects.

Improved Memetic NSGA-II Using a Deep Neighborhood Search

Elitist non-sorted genetic algorithms as part of Pareto-based multi-objective evolutionary algorithms seems to be one of the most efficient algorithms for multi-objective optimization. However, it has some shortcomings, such as low convergence accuracy, uneven Pareto front distribution, and slow convergence. A number of review papers using memetic technique to improve NSGA-II have been published. Hence, it is imperative to improve memetic NSGA-II by increasing its solving accuracy. In this paper, an improved memetic NSGA-II, called deep memetic non-sorted genetic algorithm (DM-NSGA-II), is proposed, aiming to obtain more non-dominated solutions uniformly distributed and better converged near the true Pareto-optimal front. The proposed algorithm combines the advantages of both exact and heuristic approaches. The effectiveness of DM-NSGA-II is validated using well-known instances taken from the standard literature on multi-objective knapsack problem. As will be shown, the performance of the proposed algorithm is demonstrated by comparing it with M-NSGA-II using hypervolume metric.

Optimal shutdown strategies for COVID-19 with economic and mortality costs: British Columbia as a case study

Decision makers with the responsibility of managing policy for the COVID-19 epidemic have faced difficult choices in balancing the competing claims of saving lives and the high economic cost of shutdowns. In this paper, we formulate a model with both epidemiological and economic content to assist this decision-making process. We consider two ways to handle the balance between economic costs and deaths. First, we use the statistical value of life, which in Canada is about C$7 million, to optimize over a single variable, which is the sum of the economic cost and the value of lives lost. Our second method is to calculate the Pareto optimal front when we look at the two variables—deaths and economic costs. In both cases we find that, for most parameter values, the optimal policy is to adopt an initial shutdown level which reduces the reproduction number of the epidemic to close to 1. This level is then reduced once a vaccination programme is underway. Our model also indicates that an oscillating policy of strict and mild shutdowns is less effective than a policy which maintains a moderate shutdown level.

A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm

The flue gas desulfurization process in coal-fired power plants is energy and resource-intensive but the eco-efficiency of this process has scarcely been considered. Given the fluctuating unit load and complex desulfurization mechanism, optimizing the desulfurization system based on the traditional mechanistic model poses a great challenge. In this regard, the present study optimized the eco-efficiency from the perspective of operating data analysis. We formulated the issue of eco-efficiency improvement into a many-objective optimization problem. Considering the complexity between the system inputs and outputs and to further reduce the computational cost, we constructed a Kriging model and made a comparison between this model and the response surface methodology based on two accuracy metrics. This surrogate model was then incorporated into the NSGA-III algorithm to obtain the Pareto-optimal front. As this Pareto-optimal front provides multiple alternative operating options, we applied the TOPSIS to select the most appropriate alternative set of operating parameters. This approach was validated using the historical operation data from the desulfurization system at a coal-fired power plant in China with a 600 MW unit. The results indicated that the optimization would cause an improvement in the efficiency of desulfurization and energy efficiency but a slight increase in the consumption of limestone slurry. This study attempted to provide an effective operating strategy to enhance the eco-efficiency performance of desulfurization systems.

Pareto-optimal front for multi-objective optimization of the suspension of a full-vehicle model in the frequency domain

PurposeThis study aims to propose a complete and powerful methodology that allows the optimization of the passive suspension system of vehicles, which simultaneously takes comfort and safety into account and provides a set of optimal solutions through a Pareto-optimal front, in a low computational time.Design/methodology/approachUnlike papers that consider simple vehicle models (quarter vehicle model or half car model) and/or simplified road profiles (harmonic excitation, for example) and/or perform a single-objective optimization and/or execute the dynamic analysis in the time domain, this paper presents an effective and fast methodology for the multi-objective optimization of the suspension system of a full-car model (including the driver seat) traveling on an irregular road profile, whose dynamic response is determined in the frequency domain, considerably reducing computational time.FindingsThe results showed that there was a reduction of 28% in the driver seat vertical acceleration weighted root mean square (RMS) value of the proposed model, which is directly related to comfort, and, simultaneously, an improvement or constancy concerning safety, with low computational cost. Hence, the proposed methodology can be indicated as a successful tool for the optimal design of the suspension systems, considering, simultaneously, comfort and safety.Originality/valueDespite the extensive literature on optimizing vehicle passive suspension systems, papers combining multi-objective optimization presenting a Pareto-optimal front as a set of optimal results, a full-vehicle model (including the driver seat), an irregular road profile and the determination of the dynamic response in the frequency domain are not found.

Numerical simulation and microchannels parameters optimization for thermal management of GaN HEMT devices

Purpose This study aims to satisfy the thermal management of gallium nitride (GaN) high-electron mobility transistor (HEMT) devices, microchannel-cooling is designed and optimized in this work. Design/methodology/approach A numerical simulation is performed to analyze the thermal and flow characteristics of microchannels in combination with computational fluid dynamics (CFD) and multi-objective evolutionary algorithm (MOEA) is used to optimize the microchannels parameters. The design variables include width and number of microchannels, and the optimization objectives are to minimize total thermal resistance and pressure drop under constant volumetric flow rate. Findings In optimization process, a decrease in pressure drop contributes to increase of thermal resistance leading to high junction temperature and vice versa. And the Pareto-optimal front, which is a trade-off curve between optimization objectives, is obtained by MOEA method. Finally, K-means clustering algorithm is carried out on Pareto-optimal front, and three representative points are proposed to verify the accuracy of the model. Originality/value Each design variable on the effect of two objectives and distribution of temperature is researched. The relationship between minimum thermal resistance and pressure drop is provided which can give some fundamental direction for microchannels design in GaN HEMT devices cooling.

Multi-objective optimization of the GFRP injection molding process parameters by using GA-ELM, MOFA and GRA-TOPSIS

Due to the influence of injection molding process, warpage and volume shrinkage are two common quality defects for products manufactured by the glass fiber-reinforced plastic (GFRP) injection molding. In order to minimize the two defects, the extreme learning machine optimized by genetic algorithm (GA-ELM), multi-objective firefly algorithm (MOFA) and a multi-objective decision-making method called GRA-TOPSIS are implemented in this study. All experiments based on Latin hypercubic sampling (LHS) are conducted by Moldflow software to obtain results of warpage and volume shrinkage. The prediction accuracy of defect prediction models based on the extreme learning machine (ELM) and GA-ELM algorithm is compared. The results show that GA-ELM models can better predict defect values. Finally, MOFA is utilized to find the Pareto optimal front, and the GRA-TOPSIS method is used to find the optimum solution from the Pareto optimal front. According to the results of the simulation verification, the warpage and volume shrinkage are effectively reduced by 12.25% and 6.11% compared with those before optimization, respectively, which indicates the effectiveness and reliability of the optimization method.