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The rapid development of urbanization in China inevitably leads to a sharp increase in building energy consumption, and Passive House (PH) have received much attention in China in recent years. In this paper, the annual energy demand (AED) and summer discomfort hours (SUH) of PH are used as optimization objectives to establish a set of design solutions for multi-objective optimization (MTO) and economic analysis of a PH in Shandong Province, China. It is found that (1) the algorithm based on nearest neighbor component analysis (NCA) feature extraction applies to the sensitivity analysis of multivariate nonlinear high-dimensional data, and the NCA-GA-RBF neural network technique is proposed, which has significantly improved the goodness of fit and generalization ability for AED and SUH. (2) The NSGA-II genetic algorithm is used for the optimization search, and the results show that the overall improvement rates of AED and SUH are 25.5% and 21.6%, respectively, among the nine solutions after comprehensive evaluation; (3) Finally, the optimal scheme of energy saving and the optimal scheme of the economy are selected for economic evaluation. By comparing the economics of the two options and the improvement rates of AEDs and SUHs, the guidelines for design decision-makers are provided. In summary, the design scheme of this paper is forward-looking and reference-oriented and provides some reference suggestions for the development of PH in China. • The NCA algorithm is suitable for multivariate nonlinear sensitivity analysis. • NCA-GA-RBF has stronger and more stable generalization ability and accuracy. • In this paper, climate is the main factor affecting thermal parameters. • Global costs have a stronger economic interpretation. [ABSTRACT FROM AUTHOR]

The energy crisis caused by climate change is the most significant challenge of the 21st century. Single-objective and multi-objective optimization studies for buildings often only consider the building itself, without considering the impacts of climate change. However, in the future, energy-efficient design and appropriate operating parameters will be crucial considerations in the face of global climate change. This paper aims to discuss the impact of climate change in the humid-hot region over the past 60 years on the energy consumption of office buildings and propose countermeasures. Firstly, the meteorological data of Guangzhou from 1960 to 2020 is statistically analyzed, and four typical meteorological years and corresponding air conditioning design days are established. Secondly, Energyplus is used to simulate the building's energy consumption, and the energy consumption factors are analyzed for sensitivity using jEplus. Finally, a multi-objective optimization is performed using jEPlus + EA with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The results found that climate change led to a significant increase in the duration of high outdoor temperatures during summer. Specifically, the daily dry-bulb temperature for air conditioning design increased by 0.4–0.7 °C, and due to climate change, the energy consumption of the office building increased by about 4.6 %. The main factor affecting the cooling energy consumption in summer was the indoor cooling set point (8.29%–30%), infiltration air volume (1.0%–8.6%), fresh air volume (3.2%–16.06%), the glazing solar transmission rate (1.25%–9.0%), building orientation (0%–1.43%), and relatively small influence of envelope insulation performance. The operation scheme determined by multi-objective optimization can reduce the cooling energy consumption of the building by 10.6%–16.8%, which can mitigate the rise in energy consumption resulting from climate change to a greater extent. [Display omitted] • Discuss the impact of climate change over the past 60 years on the energy consumption of office buildings. • There was a significant increase in the duration of high outdoor temperatures during the summer. • Multi-objective optimization is performed using jEPlus + EA. • Climate change caused energy consumption to increase by about 4.6 %. • Optimal solutions can reduce the cooling energy consumption of the building by 10.6%–16.8%. [ABSTRACT FROM AUTHOR]

Abstract Green and sustainability issues are being considered extensively in industry and in the literature. Most of the green-related effects in the manufacturing sector can be modeled by the costs associated with the production machines due to their resource consumption and/or pollutant emission, as a part of the operation cost. Accordingly, taking into account the machine processing costs alongside the other production criteria is one of the main approaches to consider green issues in the production management problems. In the same way, scheduling as one of the major operational problems in the factories can benefit this point of view to involve sustainability aspects. In this paper, the aforementioned modeling approach is taken one more time to establish a formulation for a green scheduling problem in uniform parallel machine environments. Here, it is assumed that the machines have different processing cost rates, i.e. they are different with regard to sustainability. The considered problem objectives are the total green cost and the makespan which are minimized simultaneously with the aim of earning Pareto optimal solutions. To this end, the ε -constraint method is first used to convert the problem into single objective problems. Then an existing heuristic is improved to solve these problems and estimate the Pareto solutions that show the trade-offs between the green cost objective and the ordinary time objective. Moreover, having a rigorous analyze in a theorem, some useful upper bounds for the gap of the green cost of the heuristic solution from optimality is derived. Finally, conducting numerical experiments at the end of the paper, the superiority of the improved heuristic to the previous one and its effectiveness in approximating the Pareto optimal solutions is demonstrated. Graphical abstract Image 1096644 Highlights • A green scheduling model in uniform parallel machine environments is developed. • The machines are assumed to have different sustainability costs. • The aim is to minimize both the total costs and the makespan, simultaneously. • The ε-constraint method is first used to convert the problem into single objective problems. • An improved version of an existing heuristic is utilized to solve the problems. [ABSTRACT FROM AUTHOR]

Since sustainable scheduling is arousing increasing attention from many manufacturing enterprises and energy consumption is a core problem regarding sustainability, the purpose of this paper is to develop an energy-efficient scheduling method to fulfill material delivery tasks in mixed-model assembly lines. In this research, the objective of minimizing the energy consumption is jointly integrated with the operational criterions when executing material delivery tasks. Owing to the NP-hard nature of the considered problem, a Taboo enhanced Particle Swarm Optimization (TEPSO) algorithm is developed to solve the multi-objective problem. Several improving strategies are applied to enhance the performance of the proposed TEPSO in order to obtain a stronger local search capability and faster search speed. The performance of the proposed TEPSO algorithm is evaluated by comparing with two other high-performing multi-objective optimization methods. Computational experiments are conducted in order to test and verify the effectiveness and efficiency of the proposed TEPSO algorithm. The achievements reported in this paper might be inspiring for further studies on energy-efficient production scheduling. [ABSTRACT FROM AUTHOR]

Growing environmental awareness and the relevance of energy costs in many industries has led to the need of improving energy efficiency in operations management; hence, energy-aware scheduling (EAS) has grown in importance. In EAS three basic strategies can be identified. First, a large part of research activities is aimed at reducing energy consumption ; second, energy costs can be reduced by making use of varying energy prices ; third, a rarely-examined aspect is load curve leveling , used to reduce demand charges or grid utilization charges. In this paper, all three strategies are integrated into one model for the first time in order to solve a multi-objective hybrid flow shop scheduling problem. A new multiphase iterated local search algorithm (ILS) is developed to determine a three-dimensional Pareto front regarding three objectives: makespan, total energy costs and peak load. Tabu lists, several time- and energy-dependent list scheduling algorithms, a right-shifting procedure and a reference point based fitness function enable high-quality solutions. A computational study is presented that analyzes the interdependencies of objectives and compare the proposed algorithm to well-known NSGA2 heuristic. The ILS is proven to be suitable in purposeful search in the solution space, which allows practical decision support. [ABSTRACT FROM AUTHOR]

Abstract Regarding to energy efficiency retrofit investment to numerous buildings, which buildings should be invested and which of the retrofit measures should be implemented for investable buildings are challenging tasks. The current literature have studied on choosing energy efficiency retrofit measures in single building, relatively little attention has been paid to the retrofit investment decision-making in multiple buildings. In addition, the existing studies almost put the retrofit cost as an objective that need to be minimized, and the retrofit capital budget is not taken into consideration. This paper proposes a decision-making optimization framework for energy efficiency retrofit investment in numerous buildings under financing budgetary restraint. A multi-objective optimization model with the economic goals being the net present value and time of return, and the environmental goals being the energy saving and emission reduction is presented, and then the intelligent optimization method combing particle swarm optimization and genetic algorithm is designed to search the retrofit investment strategy. The obtained investment strategy could determine which of the buildings should be invested to retrofit, and the combination of retrofitting measures for every investable building. An empirical study is conducted on 27 buildings of non-governmental organization in the state of Delaware in the United States, and the results indicate that the validity of the proposed framework. The findings indicate that the framework is an effective approach to assist the sustainability goal at regional level. Graphical abstract Image 1 Highlights • A decision-making optimization framework for energy efficiency retrofit is proposed. • Retrofit investment in numerous buildings under the capital restraint is studied. • An intelligent optimization method is designed to obtain the investment strategy. • An empirical study is conducted on 27 NGO buildings in the state of Delaware of USA. • The results could well balance economic and environmental goals of sustainability. [ABSTRACT FROM AUTHOR]

Selective laser sintering technology is a method of additive manufacturing that is growing with widely application. Due to the increasing tense of energy situation, it is also timely to consider the economic and environmental issues of growth in additive manufacturing. The innovative selective laser sintering technology optimization approach proposed in this article encourages and enables the designers and users to obtain optimal sintering parameters and reduces energy consumption and cost in sintering process. This paper creates a potential approach for realizing the relationship between main sintering parameters and energy consumption and material cost. To achieve high efficiency of the process, optimization of parameters based on energy and cost consumption are investigated. A multi-objective model with optimized constraints is set up and solved by non-dominated sorting genetic algorithm II. Energy consumption and material cost are treated as the two objectives, which are affected by three variables, namely scanning speed, gap distance and layer thickness. The effectiveness of the multi-objective optimization model was verified experimentally and results are fully discussed. [ABSTRACT FROM AUTHOR]

Due to fossil fuel depletion and global warming, biomass plays a significant role in supplying the required energy in the industrial and residential sectors. In order to develop clean and efficient conversion technology, an innovative combined cooling, heating and power (CCHP) system based on biomass is introduced in this paper. The proposed system comprises a gasification process, solid oxide fuel cell, gas turbine cycle, Rankine and organic Rankine cycles, absorption chiller, and multi-effect desalination unit simulated in ASPEN. In this system, a new configuration is proposed in order to regenerate the waste heat efficiently at every level of temperature and increase energy and exergy efficiencies. Also, thermodynamics, economic, and sensitivity analyses are performed in order to investigate the energy and exergy efficiencies, the overall cost of the system, and effective parameters. The novel optimization approach (RSM-GA-TOPSIS) is introduced by combining the response surface methodology, genetic algorithm, and Shannon entropy and TOPSIS in order to model and optimize the objective functions in DESIGN EXPERT and MATLAB software, respectively. The objectives of the optimization are maximizing energy and exergy efficiencies and minimizing the overall cost of the system. As a result, based on the initial conditions, the electricity production, overall energy and exergy efficiencies, overall cost of the system, and overall exergy destruction can be acquired at 25.235 MW, 76.69%, 57.07%, 6.1063 × 106 $, and 26.03 MW, respectively. After performing the optimization method, the optimum solution (point 3) is obtained through the TOPSIS approach. In terms of thermodynamics analysis, the electricity production, energy and exergy efficiencies are acquired at 31 MW, 88.15%, and 67.30%, which are increased by 22.85%, 14.94%, and 17.93%, respectively, compared with the initial conditions of the proposed system. In terms of economic analysis of the optimum solution, the annualized total cost, COE, and COTP can be obtained at 6.594 × 106 $, 7.3857 $/GJ, and 6.7624 $/GJ, respectively. By comparing this point to the initial conditions, although the annualized total cost at this point is increased by 7.99%, the COE and COTP are decreased by 12.1% and 10.4% due to a further increase in electricity production. Therefore, for producing energy with high efficiency and low COE, the system should be operated according to the working conditions of this point (point 3). • The integration of biomass gasification, SOFC, gas turbine, Rankine cycle, ORC, absorption chiller, and MED is introduced. • This system is designed to produce electricity, heating and cooling power, as well as desalinated water. • Novel configuration and optimization method (RSM-GA-TOPSIS) are performed to optimize the system's efficiency and cost. • The energy and exergy efficiencies and the annualized cost of the system are 0.76, 0.57, and 6.10 × 106 $, respectively. • Dou to the optimization, the overall energy and exergy efficiencies are improved by 14.94% and 17.93%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes an energy-aware multi-objective optimization algorithm (EA-MOA) for solving the hybrid flow shop (HFS) scheduling problem with consideration of the setup energy consumptions. Two objectives, namely, the minimization of the makespan and the energy consumptions, are considered simultaneously. In the proposed algorithm, first, each solution is represented by two vectors: the machine assignment priority vector and the scheduling vector. Second, four types of decoding approaches are investigated to consider both objectives. Third, two efficient crossover operators, namely, Single-point Pareto-based crossover (SPBC) and Two-point Pareto-based crossover (TPBC) are developed to utilize the parent solutions from the Pareto archive set. Then, considering the problem structure, eight neighborhood structures and an adaptive neighborhood selection method are designed. In addition, a right-shifting procedure is utilized to decrease the processing duration for all machines, thereby improving the energy consumption objective of the given solution. Furthermore, several deep-exploitation and deep-exploration strategies are developed to balance the global and local search abilities. Finally, the proposed algorithm is tested on sets of well-known benchmark instances. Through the analysis of the experimental results, the highly effective proposed EA-MOA algorithm is compared with several efficient algorithms from the literature. [ABSTRACT FROM AUTHOR]

In order to reduce the energy consumption of machine tool, the process parameters were optimized by the optimization model with the cutting specific energy consumption (CSEC) and the processing time. Based on the energy consumption characteristics of the numerical control (NC) machine tool, the energy consumption module of NC machine tool was analyzed. The power composition of machining process which was based on energy consumption module was proposed, and then CSEC was given with the cutting process parameters. The optimization model to minimum cutting specific energy consumption and minimum processing time was established with the process parameters under the actual constraint conditions in the manufacturing process. The multi-objective optimization model was transformed into the single objective optimization model by introducing the subjective and objective comprehensive weights and solved by the quantum genetic algorithm. When the optimization goal was to trade off processing time and CSEC to reduce the energy consumption, the selecting of the milling parameters should consider the complex effect if the constraints could be met with, large feed speed and large milling depth could benefit if the constrains of milling process were met with. The processing energy consumption of optimized process parameters decreased 27.21% compared with that of preferred process parameters while CSEC was reduced 32.07% and the processing time was reduced 34.11%. The proposed approach in this paper provided an efficient solution to reduce the impact of the environment caused by energy consumption and to realize the sustainable manufacturing. [ABSTRACT FROM AUTHOR]

With the rise of renewable energy sources (RES) due to detrimental impacts of climate change, the potential for clean energy sources such as hydrogen has gained attention. The development of various technologies, such power-to-gas (P2G) and hydrogen-based energy systems (HES), enables the integration of these systems with conventional ones in this task. This approach will help improve efficiency and resolve the substantial carbon emission problem on the demand and supply sides. This paper focuses on using hydrogen as a primary source of energy and uses wind turbine (WT) and Photovoltaic (PV) as RES. Hot- and chilled water storages besides a HTS are implemented to improve energy efficiency. Upstream grid and hydrogen markets are connected to this system to compensate for the energy deficiency in some periods. P2G functions as a carbon recycling system by reducing carbon emissions and generating extra heat from its chemical reaction for end-users. The proposed hydrogen-based energy system combined with Power-to-gas (HES-P2G) is based on stochastic programming, and takes into account various uncertainties. The nonlinearity in some restrictions is converted using the piecewise linearization technique. The epsilon-constraint method is used as a multi-objective optimization to achieve both economic and environmental goals. The fuzzy satisfying technique is used to obtain a trade-off solution between two objectives. The numerical results show the impacts of using P2G in HES in reducing carbon emission and operating cost. The current HES can significantly reduce carbon emissions; however, using P2G influences environmentally and economically. P2G will thereby cut carbon emissions by 14.2% and operating expenses by 11.7%. [Display omitted] • Multi-objective optimization of a hydrogen-based renewable penetrated multi energy system. • Investigating the impacts of carbon capture system on recycling the carbon emission of system. • Using the scenario-based approach for uncertainty modeling of stochastic resources and loads. • Using the fuzzy-based method for satisfying the objectives and obtaining an optimum point. [ABSTRACT FROM AUTHOR]

Arc welding is a common joining method, which is usually characterized by high energy consumption and low energy efficiency. With the recent focus on energy management and carbon emissions, energy saving has become a priority for manufacturing industry. In the past, energy saving technologies for welding had primarily aim for heat source improvement, with less emphasis on parameter optimization. It is obvious that parameter optimization methods for energy reduction can be applied to existing equipment where large investments are not required. Therefore, a multi-objective optimization method based on Fitness Sharing Genetic Algorithm (FSGA) is proposed for energy reduction and thermal efficiency improvement of arc welding process in this paper. Two objectives including energy consumption and thermal efficiency are considered in the optimization model with two independent variables, namely welding current and welding velocity. Additionally, the limits of the variables and welding quality are also considered. A case study of rail track joints using Shielded Metal Arc Welding (SMAW) is conducted for the verification of the proposed optimization method. Finally, the optimization method and results are analyzed with the actual data and Genetic Algorithm (GA) respectively. Comparison with actual data shows that the proposed approach has a more significant effect on energy saving and thermal efficiency improvement. The optimization analysis shows that FSGA has a better population diversity and global search capability compared with GA. [ABSTRACT FROM AUTHOR]

Hybrid flowshop scheduling problem is a hot research topic, and is widely applied for production shop or line in chemical industry, metallurgical industry, semiconductor manufacturing and other industries. However, on the one hand, the uncertain events are inevitable in actual production, which will disrupt the production plan. On the other hand, nowadays the energy problem becomes more and more serious, and attracts much attention in the manufacturing industry. Therefore, an energy-efficient hybrid flowshop rescheduling problem under the machine breakdown is addressed in this paper. Firstly, the mathematical model for the problem is established, and an energy saving strategy based on problem model is designed, which can ensure the reduction of energy consumption without affecting the production efficiency. Then, an improved multi-objective firefly algorithm is proposed to optimize the production efficiency, energy consumption and production stability. To express the problem characteristics, a two-level encoding mechanism is used to describe the individual, and a corresponding decoding mechanism is presented to generate the scheduling scheme. By simulating the location updating law of the fireflies, the population updating rule is designed, in which the variable neighborhood search is employed to avoid the local optimal. To ensure the quality of the solution set, the fast non-dominated sorting method and elite individual reserving strategy are introduced to the population evolution. Finally, the numerical experimental results indicate that the designed energy saving strategy is effective, and the proposed algorithm obtains better Pareto frontier and performs the better convergence and diversity comparing with MOEA/D and NSGA-Ⅱ, the common algorithms to solve complex multi-objective optimization problem. • A multi-objective energy-efficient hybrid flowshop rescheduling problem is studied. • The problem characteristics of dynamic event and energy consumption are analyzed. • An improved multi-objective firefly algorithm is proposed and tested by experiments. • An energy saving strategy based on the processing speed selection is designed. • The total energy consumption is reduced without affecting the production efficiency. [ABSTRACT FROM AUTHOR]

Choosing the optimum cutting parameters is regarded as one of the important energy saving technologies. This paper presents a method for complex optimization of cutting parameters with the objectives of energy efficiency and processing time, which integrates Taguchi method, response surface method (RSM) and multi-objective particle swarm optimization algorithm (MOPSO). In this study, specific energy consumption (SEC) is selected to evaluate energy efficiency and the calculation model is introduced firstly. Then the method is described in detail. Taguchi is used to design the experiment, signal-to-noise (S/N) ratio is subsequently employed to analyze the performance of parameters on SEC and processing time, and the significant contributions of parameters can be determined by use of range analysis method. RSM is conducted to develop regression models for the responses based on the experimental data, and the optimal machining parameters for minimizing energy and time are determined through the modified MOPSO algorithm. Finally, four machining parameters schemes with different optimization objectives are compared, and the results show that a trade-off point can be drawn between the low processing time and high energy efficiency. [ABSTRACT FROM AUTHOR]

This paper aims to develop an optimal real-life energy-efficient design for a production plant within the concept of the circular economy. The problem is to install a Heat Recovery System (HRS) that utilizes the hot oil used by the compressors to heat the water for the central heating system. To achieve the desired level of energy efficiency this design problem must be formulated from both the optimization and sustainability points of view. Additionally, this design problem must also consider the investment cost. In line with this purpose, this paper formulates this design problem as an optimization problem employing a mathematical programming approach as a single objective, and as a multi-objective optimization problem through a goal programming approach. Besides, this paper uses the return on investment as a key performance indicator, since it deals with a real-life design problem with an investment cost. The related design problem is solved with the single objective and multi-objective versions of the developed mathematical programming model via a commercial solver to identify different design alternatives and hence giving the decision-maker to make a selection option. Finally, the capability of the developed mathematical programming model is tested on a set of randomly generated problems. The obtained results indicate that the developed mathematical programming model is a successful decision support system since its single and multi-objective versions are capable of identifying energy-efficient production designs within the context of the real-life problem on hand and the circular economy. • A real-life energy-efficient design problem for a production plant is undertaken. • A heat recovery system is proposed within the concept of a circular economy. • The problem is modelled from both the optimization and sustainability aspects. • The problem is formulated as multi-objective models using goal programming. • The models are solved to minimize investment costs and maximize energy efficiency. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing concern over the environmental impact of traditional manufacturing, especially in terms of energy consumption and related emissions of carbon dioxide. Besides the adoption of new equipment, production scheduling could play a key role in reducing the total energy consumption of a manufacturing plant. In this paper, we explicitly introduce the objective of minimizing energy consumption into a typical production scheduling model, i.e., the job shop scheduling problem, based on a machine speed scaling framework. To solve this bi-objective optimization problem, we propose a multi-objective genetic algorithm incorporated with two problem-specific local improvement strategies. These local improvement procedures aim to enhance the solution quality by utilizing the mathematical models of two restricted subproblems derived from the original problem. Comprehensive computational experiments have been carried out to verify the effectiveness of the proposed solution approach. The results presented in this work may be useful for future research on energy-efficient production scheduling. [ABSTRACT FROM AUTHOR]

With the improvement of occupants' requirements for quality of life, the functions and regions of buildings are becoming more and more refined. Large-scale buildings with multi-regional function come out increasingly. The traditional uniform constant temperature design and operation management technology can no longer meet the needs of occupants, as the dynamic thermal comfort level of human body in different regions has great discrepancy. This paper conducts the whole chain regulation model for large multi-regional buildings from demand level, application level and control level. Aiming to reduce the load reasonably on the premise of thermal comfort, a model of dynamically adjusting the set point temperature in different regions is established on demand level. As load demand refers to the energy provided by the operation of HVAC system, the controlled parameters values are obtained by adopting the intelligent optimization strategy for the application level. On the bottom control level, the optimized parameters are managed by model predictive control (MPC), which has great advantage of rapid response. It is found that the strategy of dynamically adjusting the temperature set point in typical day can reduce the load demand by 6.16% without sacrificing the comfort of indoor personnel. Based on the load demand optimization, the operation optimization and MPC strategy is further adopted for application and control level by simulation, and can realize the total energy saving of HVAC system by 12.78%. • A unified whole chain regulation model for multi-regional building is provided. • The multi-regional dynamic indoor set point temperature model is established. • The intelligent optimization regulation model is obtained on application level. • A fast parameter regulation control model is established on control level. • The strategy achieves multi-objective optimization of energy and thermal comfort. [ABSTRACT FROM AUTHOR]

Energy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. However, the ever increasing demand of customers for quality has led to better surface finish and thus more energy consumption. The energy efficiency of machines tools is generally very low particularly during the discrete part manufacturing. This paper provide a multi-objective predictive model for the minimization of power consumption and surface roughness in machining, using grey relational analysis coupled with principal component analysis and response surface methodology, to obtain the optimum machining parameters. The statistical significance of the proposed predictive model has been tested by the analysis of variance (ANOVA) test. The obtained results indicate that feed is the most significant machining parameter followed by depth of cut and cutting speed to reduce power consumption and surface roughness. The constructed response surface contours can be used by the shop floor people to find and use the best combination of machining parameters for the given situation. The reduction of peak load through optimization will results in lowering the power consumption of the machine tools during non-cutting idling time. [ABSTRACT FROM AUTHOR]

Abstract: Reducing energy usage is an essential consideration in sustainable manufacturing. In the past, metal cutting operations have been mainly optimized based on economical and technological considerations without the environmental dimension. It is essential to improve production rate and cutting quality while simultaneously mitigating the effect of manufacture on the environment. This paper presents a multi-objective optimization method based on weighted grey relational analysis and response surface methodology (RSM), which is applied to optimize the cutting parameters in milling process in order to evaluate trade-offs between sustainability, production rate and cutting quality. Three objectives, such as surface roughness, material removal rate and cutting energy, are simultaneously optimized. The parameters evaluated are spindle speed, feed rate, depth of cut and width of cut. The grey relational grade values for the multiple responses are obtained using weighted grey relational analysis. The weighted grey relational analysis is a quantitative method we proposed to determine the weight factors of multiple responses for grey relational analysis. Based on weighted grey relational analysis and RSM, the optimal milling parameters were identified, and the obtained results indicated that width of cut is the most influential parameter. Experiments using Taguchi design method were performed to verify the proposed optimization method and promising results were achieved. The experimental results indicate that the proposed optimization method is a very useful tool for multi-objective optimization of cutting parameters. Moreover, the results also show that low spindle speed cutting is more energy efficient than cutting at initial speed for milling process and the traditional multi-objective optimization does not satisfy the requirement for sustainable machining. [Copyright &y& Elsevier]

Despite the fact that research on multigeneration and polygeneration systems are available, more efficient and effective systems that fulfill several energy demands at the same time are still needed. Furthermore, efficient use and harvesting of renewable energy sources are critical for global emission reductions. Accordingly, this paper proposes a novel geothermal-solar-based hybrid system for simultaneous power and freshwater production. The overall objective of the study is to model the proposed cogeneration system with energy and exergy exergoeconomic, and economic viewpoints, and obtain the optimum performances of the system using non-dominated sorting genetic algorithm II (NSGA-II) method and LINMAP decision-making approach. According to the obtained results, at the base operating conditions, the exergy destruction rate for the CPVT is the highest with 311.3 k W amongst other components, followed by the RO unit with a value of 159 k W. Also, the net output electricity of the total system and freshwater generation rate are computed to be 775.3 k W and 10.14 k g. s − 1 . Also, for the first optimization scenario (S U C P − η e x ), the exergy efficiency and sum unit cost of the products are obtained by 42.31 % and 16.32 $. G J − 1 , respectively. And, the produced electricity and freshwater are computed to be 755.3 k W and. 16.44 k g. s − 1 • Proposal and study of a newly designed electricity/freshwater co-production system. • Smart use of solar and geothermal energies for the performance enhancement. • Energy, exergy, exergy costing, and NPV methods together with NSGA-II optimization. • The optimum exergy efficiency and unit cost are separately 42.31% and 16.32 $/GJ. • The optimum payback period of the system is estimated to be 1.07 years. [ABSTRACT FROM AUTHOR]

Nowadays, due to the raising research interest in net and nearly zero-energy concepts at community level, great efforts are spent to investigate self-sustaining and energy-independent communities. Small and off-grid islands represent one of the best places to focus the attention on, and to suit pilot projects of self-sufficient energy communities. In this context, the dynamic simulation represents a useful tool for designing, analysing, and optimizing islands' energy system layouts, especially in case of renewable energy system adoption (due to the performance unpredictability of renewable energy sources). Therefore, powerful yet flexible dynamic simulation tools are increasingly in demand. In this framework, this paper presents a novel dynamic simulation model, developed in TRNSYS environment, purposely conceived to investigate innovative technologies, novel system layouts, and advanced control logics, in the case of small and off-grid islands, aiming at reaching the nearly/net-zero energy goal at a community level. To prove the potential of the novel dynamic simulation tool, a suitable case study referring to the existing island community of El Hierro (Canary Island) was considered and investigated. Specifically, to enhance the island energy independence, diverse scenarios and system layouts, including different renewable energy based technologies (e.g. wind turbines, hydroelectric power plants, solar thermal collectors, etc.) have been investigated by the novel developed simulation tool. Results demonstrate the potentiality of the adopted method as well as the feasibility of the proposed system layouts. Specifically, the investigated scenarios provide up to 85% of the annual electricity demand and about 79% of the annual thermal energy needs by renewable energy sources with substantial economic savings. [Display omitted] • Modelling and simulation of small islands hybrid multi-generation energy systems. • Using dynamic simulation to enhance off grid-islands energy independence. • Development of a design tool to boost renewable energy penetration of small islands. • Energy, economic and environmental optimization of islands' grid energy fluxes. • Exploitation of energy storage to mitigate fuel consumption in off-grid communities. [ABSTRACT FROM AUTHOR]

The building envelope performance significantly influences the building energy consumption, and the major influencing factors of the building envelope include the external wall U-value, external wall solar absorptance, roof U-value, roof solar absorptance, external window U-value, and window-to-wall ratio. This paper proposed a framework that integrates Building Information Modeling (BIM) with LSSVM (least square support vector machine (LSSVM) and non-dominated sorting genetic algorithm-II (NSGA-II) to study the influence of building envelope parameters on building energy consumption and discover the best design. The DesignBuilder (DB) is used to perform energy consumption simulation and orthogonal experiment to obtain the sample data set. The LSSVM is utilized to learn the data set to establish a prediction model between the building envelope and energy consumption, which is used as the fitness function of the building energy consumption in NSGA-II. With the objective of minimizing building energy consumption and maximizing the indoor thermal comfort (measured by predicted mean vote or PMV), an NSGA-II multi-objective optimization model is established. The Pareto front is obtained through calculation, and the optimal combination of building envelope parameters is discovered using the ideal point method. A case of a proposed school building in China is used to demonstrate the feasibility and effectiveness of the developed framework, the results show that: (1) The LSSVM has great ability to predict building energy consumption, achieving an accurate prediction with a goodness-of-fit of 0.9549 and an RMSE of 0.0273; (2) The optimized envelope design parameters of the proposed building are presented, including the External wall U-value is 0.2, the External wall solar absorptance is 0.55, the Roof U-value is 0.2, the External window U-value is 0.4166, the External window U-value is 1, the Window-wall ratio is 0.215. (3) The developed LSSVM-NSGA II hybrid approach can effectively improve the building's energy consumption and thermal comfort with the building energy consumption reduced by 10.6% and the thermal comfort increased by 32.2% than the initial values, respectively. The proposed LSSVM-NSGA II framework is able to optimize building envelope design parameters than its precedent, which can provide effective thinking for similar problems. • LSSVM-NSGA-II hybrid method can predict and optimize the building energy consumption. • The energy consumption prediction surrogate model trained by LSSVM is an efficient NSGA-II fitness function. • NSGA can effectively reduce building energy consumption and improve thermal comfort at the same time. [ABSTRACT FROM AUTHOR]

Dielectric medium (kerosene) between the tool electrode and workpiece forms a reaction product, owing to thermal effect of discharge in the process of electrical discharge machining (EDM), which is emitted from the surface in the form of harmful gas and may get into the operator's body by breath system inhalation and contact with skin. Therefore, exhaust emissions should be considered as important performance indicators for the green machining in the process of EDM. Moreover, energy efficiency and machining productivity should be improved and the cut process must take ecological constraints into consideration. Thereafter, the definition of green manufacturing helps to evaluate different and sometimes conflicting aspects of the manufacturing process, such as machining productivity, surface quality, energy efficiency and exhaust emissions. But it is difficult to dynamically adjust the weights of different aspects according to the actual processing conditions. This paper proposes a framework for optimization of cutting parameters to minimize energy consumption and exhaust emissions during electrical discharge machining of Al 6061 and SKD 11. Three cutting process parameters (peak current, pulse period, and duty cycle) are tested against material remove rate (MRR), surface roughness (Ra), energy efficiency per volume (EEV), and exhaust emissions characteristics (EEC) for EDM of these two materials. First, the process energy and exhaust emissions are analyzed. The results show that EEV is negatively correlated with surface quality (Ra) for both Al 6061 and SKD 11 material. Second, two data analyses (Taguchi and adaptive-network-based fuzzy inference system (ANFIS) analyses) are utilized to obtain high MRR and best Ra, EEV and EEC for the desired machining performances and environmental sustainability. It is found that the relative errors between the predicted values and experimental results of MRR, Ra, EEV and EEC are all not more than 20%, and found that the maximum of mean relative errors of them is 13.04%. Finally, a framework implemented by an optimization system, based on ANFIS, enhanced cuckoo search (ECS) algorithm and desirability function, is built to obtain the desired cutting parameters. It is found that high peak current (8.94A) and pulse period (500 μ s) are recommended to obtain multi-objective optimization for machining Al 6061, and that the optimal solutions of the EEV and EEC are significantly decreased, by 87.13% and 37.09%, respectively. Similarly, high peak current (8.77A) and pulse period (261.5 μ s) are recommended to obtain multi-objective optimization for machining SKD 11, and the optimal solutions of the EEV and EEC are significantly decreased, by 93.73% and 46.85%, respectively. Thereafter, it can be concluded from the above research that the proposed technique offers significant advantages and potential for applications in the green manufacturing field. Image 1 • Energy efficiency per volume was negatively correlated with Ra. • Maximizing energy efficiency and minimizing exhaust emissions by empirical models were obtained. • Balancing the machining performances and environmental sustainability was considered. • An optimization system with manual multi-objective weights was build. • Green regions of exhaust-friendly and energy-friendly were acquired. [ABSTRACT FROM AUTHOR]

Process route planning is vital for implementing energy saving and low-cost production in mechanical processing as it can directly affect the energy consumption and the cost of mechanical product processing. Therefore, a multiobjective optimization approach of machining center process routes to realize energy saving and low-cost mechanical processing is proposed in this paper. To provide theoretical support for this study, process route optimization problems of a machining center are analyzed, the concept of workstep element is introduced to represent the features of machined parts, and a multiobjective optimization model is established. The optimization model is solved based on the combination of a workstep chain intelligent generation algorithm and a non-dominated sorting genetic algorithm II. Finally, the emulsion pump case process route is used as a case study to verify the feasibility and practicability of the proposed method. Comparison with actual data shows that with the single objective of energy consumption and processing cost, based on the multiobjectives of energy saving and low cost as the optimization goal, the energy consumption was 1.018 × 107 J, and the processing cost was CNY32.65. Compared with the other two experimental results, the energy consumption and the processing cost demonstrated the best comprehensive performances, consistent with energy saving, low cost and sustainable production, thereby validating the established model. Furthermore, the optimization analysis shows that the combinatorial optimization algorithm has a better solution speed and optimization precision than the general genetic algorithm. • Analyzed the underlying effects of the mechanism of process route optimization. • A multiobjective optimization model of the machining center process route was built. • The concept of workstep element was proposed. • An intelligent combinatorial optimization algorithm for the workstep chain was proposed. [ABSTRACT FROM AUTHOR]

One important opportunity to reduce energy waste and global warming is renovation, retrofit, and refurbishment of existing buildings. Adding a multi-layer internal insulation is an efficient way to upgrade an existing housing into a nearly zero-energy buildings, NZEB. There is a vast variety of products and solutions for the building envelope with different environmental impacts. In this paper a methodology is presented to establish a two-criteria framework to rank thermal insulation materials to be used in the envelope. The objective was to design an optimal insulation system based on a multi-objective approach to maximize the energy saving and minimize the environmental impact. Using twelve typical insulation materials with different thicknesses, a four-layer insulation system was developed and applied to a Case study, a typical multi-story apartment building located in a hot summer and cold winter zone. Results revealed there are several optimum solutions to significantly reduce the building energy consumption, with important differences in their environmental impacts. For the case study, eight Pareto-optimal solutions were derived using the two-objective optimization offering more than 70% reduction in the energy demand, and a global warming potential range of 192–1500 kgCo2 eq. [ABSTRACT FROM AUTHOR]

The traditional synthesis gas (syngas) production process in the coal chemical field encounters the problems of high cold energy consumption and difficulty in effectively capturing CO 2. This limits the clean production and sustainable development of coal chemical industry. To solve this problem, this paper proposes a novel process for capturing and separating CO 2 from syngas using [bmim][Tf 2 N]. This process can effectively reduce the cold energy consumption and improve the CO 2 capture efficiency, which will help relieve the global concerns over CO 2 emissions. Energy, economic, and environmental analyses of the process were conducted based on accurate modeling and simulation results. To balance the conflict between the economic and environmental performances, the non-dominated sorting genetic algorithm II method was used for the multi-objective optimization of the process. The CO 2 escape flow rate and total annual cost were the objective functions of the multi-objective optimization scheme and had values that were 5.9% and 1.5% higher than their minimum values, respectively. Moreover, the multi-objective optimization scheme for this process was compared with the Rectisol process and CO 2 capture from syngas using [emim][Tf 2 N]. This work provided new ideas for clean production of coal chemical industry, and also provided reference for the green sustainable development of relevant work. • A novel [bmim][Tf 2 N]-based CO 2 capture and separation process at 25 °C is proposed. • Energy, economic, environmental comprehensive analyses are used to study the process. • Multi-objective optimization balances the conflicting objectives of this process. • The multi-objective optimization process has feasible CO 2 capture ratio (96.34%). [ABSTRACT FROM AUTHOR]

Presented in this paper is a methodology for evaluation of dry turning process along with optimal machining parameters, which allows a compromise between technological, economical, and ecological requirements. Dry longitudinal turning of Ni-based superalloy - Inconel 601 workpiece using three turning inserts coated with TiAlN + AlCr 2 O 3 by physical vapour deposition was investigated. The machining was performed at three different levels of cutting speed, feed, and depth of cut. During machining, power characteristics were measured in the form of current and interphase voltage. Machined parts and turning inserts were tested for surface roughness and tool life, respectively. Machining times, power and energy consumption were also calculated. Regression models were defined for arithmetical mean roughness, flank wear, and power. Negative environmental impact of the cutting process was monitored through midpoint and endpoint life cycle assessment (LCA). Finally, multi-objective optimization of output functions was performed. Optimization problem of minimizing the objective function was solved for eight output functions which characterize the process. The objective functions within this problem are: minimization of surface roughness, minimization of flank wear, minimization of cutting time, minimization of energy consumption, and minimization of four LCA indicators. The results indicate that dry turning of Ni-based superalloy - Inconel 601 requires higher turning inserts radii, higher cutting speeds, and smaller feeds and depths of cut. This allows acceptable flank wear of turning insert and required surface quality of workpiece, while drastically reducing cutting time, energy consumption and negative impact on the environment. The results also indicate that, within the set limitations and conditions of this experiment, increase of cutting speed can be a good strategy for reducing energy consumption and diminishing the impact of machining on the environment. • The longitudinal turning of an Inconel 601 in dry environment is presented. • Current, voltage, mean roughness, and flank wear are measured; while time, power and energy consumption are calculated. • Life cycle assessment is applied to quantify the negative environmental impacts. • Multi-objective optimization is applied to evaluate input variables of the process. • The results indicate that increasing cutting speed can be a good strategy for cleaner production. [ABSTRACT FROM AUTHOR]

Seawater air conditioning (SWAC) systems use deep seawater as a cooling utility. SWAC systems are receiving increasing attention as an integral component of sustainable cooling units of lodging complexes because of the availability of seawater, the substitution of chemicals refrigerants, low achievable temperature, low environmental impact, and large potential savings in energy consumption compared to conventional air-conditioning systems. The design of these systems depends mainly on the energy demand of each hotel in each month; however, there is uncertainty in this variable, since it depends on the season of the year, being lower in months of low occupancy and higher in months of high tourist occupancy. This paper presents a multi-objective optimization with a probabilistic formulation approach for designing these systems under seasonal energy demand uncertainty. The model seeks to meet the demands of air conditioning in hotels using deep seawater considering technical aspects and seeks to minimize the total annual cost while minimizing emissions of greenhouse gases. The results show the importance of accounting uncertainty in the design of these systems. • a multi-objective multi-scenario stochastic model for the optimal design of SWAC networks is presented • SWAC systems are an environmentally friendly alternative to conventional AC systems • The flexibility in the operation of the system can compensate the uncertainty in the energy demand [ABSTRACT FROM AUTHOR]

Spindle system is the major mechanical component in machine tools, and its performance is responsible for a significant portion of the total consumed energy of machine tools. Conventional design optimization of spindle system is partially focused on parameter optimization of spindle motor or transmission system, contributing to an increase of the motor efficiency. Given that concurrent interactions among them is complex, very little efforts has been done to conduct integration optimization for optimum energy efficiency. To this end, a new approach of spindle system design is presented with consideration of the above two aspects adequately, to achieve the maximum energy and material efficiency. Firstly, the energy characteristic of spindle system is explicitly modelled on the basis of energy flow analysis. Then, a multi-objective optimization model for parameter optimization of spindle motor and transmission system is developed to take the both maximum energy efficiency and minimum volume as objectives, which is subjecting to a set of constraints with related to the cutting parameters boundary, processing requirements and shifting power losses. Finally, a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm is presented to solve the developed optimization model. The performance of the proposed design approach of lathe spindle system is demonstrated through different working conditions. The experimental results indicate that the design of energy and material efficient machine tools can be achieved. [ABSTRACT FROM AUTHOR]