Economic Optimal Design Research Articles

Environmental and economic optima of solar home systems design: A combined LCA and LCC approach

This paper compares the economic and environmental optimal design of Solar Home Systems (SHSs) and explores the role of economic incentives (such as tariffs and technology costs) in approximating the two optima. To achieve that, we present a methodology for the environmental and economic evaluation of grid-connected SHSs: user-scale electric systems involving a photovoltaic (PV) power system and a battery energy storage system. The proposed methodology is based on a mixed integer linear programming (MILP) optimization, life cycle assessment and life cycle costing. This methodological framework is applied to a case study involving a typical SHS installation in Italy. The results of the environmental optimal design brought to the evaluation of a 3.25 kW PV assisted by 8.66 kWh of nickel cobalt manganese batteries, whereas the costs of the SHS are minimized by a small PV system (less than 1 kW). Results underline that the environmental optimal configurations rely on battery technologies, which entails a significant cost compared to the grid connection. In contrast, the economic optimal design solutions is less impactful than the grid mix both from an environmental and economic points of view. Thanks to a reduction of batteries and PV costs, the environmental impact of the economic optimal design is expected to decrease in the future.

Economic Optimization Design of CO2 Pipeline Transportation with Booster Stations

Carbon capture and storage (CCS) technologies have widely emerged as a critical greenhouse gas reduction solution for closing the energy gap, while the world makes continuous efforts toward develop...

Environment and economy based optimization of CWS featured air coolers

Cooling water system (CWS) is a very common system that discharges industrial waste heat. For the existing researches about industrial CWSs, most of them focus on economic performance, ignoring the environmental impact. Based on the industrial foundation, this work proposes a methodology to optimize and evaluate the economic performance and environmental impact. This paper integrates several components, such as cooler network, pump network, cooling tower and air cooler to optimize the total annual cost. Carbon footprint and water footprint are introduced as environmental indicators. The whole system is integrated into Mixed Integer Nonlinear Programming (MINLP) model and solved by GAMS. Through case study, an improved CWS structure chart is obtained, and the relationship between carbon footprint, water footprint and economic performance is drawn, showing the trade-off between the three. The result shows that total annual cost (TAC) can be reduced by 9.84% by economic optimization design. The result also gives a guideline on how to improve environmental performance according to water footprint and carbon footprint.

Sensitivity Analysis and Optimal Design of a Stator Coreless Axial Flux Permanent Magnet Synchronous Generator

In this paper, the modified initial design procedure and economic optimization design of a stator coreless axial flux permanent magnet synchronous generator (AFPMSG) are presented to improve the design accuracy, efficiency, and economy. Static magnetic field finite-element analysis (FEA) is applied to the magnetic equivalent circuit (MEC) method to increase the accuracy of electromagnetic parameters and reduce the iteration times. The accuracy and efficiency of the initial design is improved by the combination of MEC method and static magnetic field FEA in the design procedure. For the economic optimization, the permanent magnetic (PM) material volume model, which affects the cost of the AFPMSG the most, is derived, and the influence degree of the main structure parameters, to the performance, is distinguished and sorted by sensitivity analysis. The hybrid genetic algorithm that combines the simulated annealing and father-offspring selection method is studied and adopted to search for the best optimization solution from the different influence degree and nonlinear interaction parameters. A 1 kW AFPMSG is designed and optimized via the proposed design procedure and optimization design. Finally, 3D finite-element models of the generator are simulated and compared to confirm the validity of the proposed improved design and the generator performance.

Effectively designed shell‐tube heat exchangers considering cost minimization and energy management

AbstractCost optimization, which is expressed as a set of analytical variables, is a key objective in economic design approaches of shell‐and‐tube heat exchangers. This study has provided new design techniques based on tube bundle effect on the economic optimization design of shell‐and‐tube heat exchangers. Also the objective of this paper is to develop the cost estimating for the new modified shell‐and‐tube heat exchangers by introducing new objective functions. According to the results the best configuration choice will obviously be the one with the least irreversibility, that is, with the lowest exergy destruction rate and lower annual capital cost. Also, the combined reduction of annual capital investment and operating cost by the new design technique led to a decrease in the overall costs of about 10% to 24% in comparison with original design. So the proposed design technique shows potential for improvement and economic optimization of shell‐and‐tube heat exchangers.

Economic optimization design for shell-and-tube heat exchangers by a Tsallis differential evolution

This paper presents an optimization of shell-and-tube heat exchangers (STHEs) considering as objective function the minimization of the total annual cost by Differential Evolution (DE) and a novel Differential Evolution variant, denominated Tsallis Differential Evolution (TDE). Shell-and-tube heat exchangers are the most widely used heat exchanger in industrial processes and its design involves several steps, including the selection of geometrical and operating parameters. The variables used for the optimization were the shell internal diameter (Ds), the outside tube diameter (do) and the baffles spacing (B). Reductions of total annual cost about of 26.99% compared to the literature, and reductions of 14.50%, 11.50%, 6.90% and 1.15% compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO) and Cuckoo-Search Algorithm (CSA) methods, respectively, were obtained for the case 1 and reduction of costs about 54.60%, 11.70% and 9.40% compared to the literature, GA and PSO, respectively, was obtained for the second case.

Energy systems in cost-optimized design of nearly zero-energy buildings

Abstract Cost optimization is one of the key elements of the EU regulatory framework concerning the energy performance of buildings. From this economic point of view, the optimum occurs when the global cost over the lifecycle of a building is minimized, and the cost-optimal energy performance level is that related to the minimum global cost. To determine this cost-optimal level by evaluating a great number of design alternatives, it is necessary to exploit automated optimization search procedures. The work presented here concerns the application of cost-optimal methodology, as defined by European regulation, to a low-consumption single-family house in France. The calculation is performed through an iterative input-output process in a computing environment that combines TRNSYS®, transient system simulation tool, with GenOpt®, generic optimization program. The methodology that was adopted allowed around ten thousand building configurations to be simulated in a reasonable computational time. The paper focuses on how the energy system affects the technical and economic optimal design solutions of the building in two different French climate conditions.

Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger

A new design optimization approach using the gravitational search algorithm is developed to obtain optimal configuration of a shell and tube heat exchanger from economic point of view. The objective function considered for the optimization process is the total annual cost including the investment cost and the operating cost. The Gravitational Search Algorithm (GSA) is a heuristic search algorithm based on the law of gravity and mass interactions. Taking into account the importance of shell and tube heat exchangers in industrial applications and the complexity in their geometry, the GSA methodology is adopted to obtain an optimal geometric configuration. The developed algorithm is applied to two case studies and the results are compared with the original design and other optimization methods available in literature such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bio-geography Based Optimization (BBO), Cuckoo Search Algorithm (CSA) and Firefly Algorithm (FFA). The simulation results show that the operating cost can be reduced by 61.5% while the total cost can be reduced by 22.3% as compared to the original design for a shell and tube heat exchanger of heat duty 4.34MW. The comparison of the obtained results with other algorithms indicates that the GSA algorithm can be successfully applied for design optimization of a shell and tube heat exchanger from economic point of view.

Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm

This paper presents an optimization of shell and tube heat exchangers with respect to the total annual costs by a cuckoo search algorithm. The total annual cost is considered as the optimization objective so as to obtain economic heat exchanger structures. The performance of the CSA algorithm is further compared with various algorithm representative of the state of the art in heat exchanger optimization. The optimal results obtained by the CSA algorithm are much better than the best solutions obtained by the existed methods. Two case studies show the operating costs can be reduced by 77% and 48% compared to the results obtained from PSO (Particle Swarm Optimization) and GA (Genetic Algorithm), respectively.

Systems Models for Mining Under Water Hazard

Systems models for the following four elements of mining design under water hazard are shown: underground inflow forecasting, economic optimal design and reliability analysis of minewater control works, and regional multiobjective planning. Deep mining in a karstic aquifer is considered where mining - aquifer interaction is modeled on a regional scale by a deterministic finite difference scheme, and on a local scale by a stochastic forecasting method. Bayesian analysis, simulation, failure tree method of reliability estimation, dynamic programing, multiobjective decision-making are the basic tools for model solution. It is believed that modeling principles as shown can be used for other mining engineering purposes.