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Abstract: The systematic, transparent and accurate on-line energy information of the production process is quite the basis of mill energy management and conservation. An Energy Management System (EMS) was built according to the characteristics of the energy usage in a typical newsprint paper mill. The EMS first realized the process data acquisition and integration, second fulfilled the on-line energy information calculation from the integrated data. A water and steam properties query component based on IAPWS-IF97 was developed and plugged into the EMS to facilitate the energy calculation of water and steam. The practical performance in the paper mill showed that the integrated energy information of the whole mill was gained and the on-line energy supervisory was achieved. In the end a systematic, transparent and accurate energy usage profile is obtained and it would provide the valuable fundamental energy data for the further energy analysis and optimization in the EMS. [Copyright &y& Elsevier]

Abstract: A detailed mathematical process integration model of a pulp and paper mill in the Northern Sweden has been developed. The main objective of this work has been set to describe the practical development of the model with particular emphasis on the development of the digester and evaporation plant sub-models. Actual plant measurements have been used to validate the model. By implementing the sub-models into the complete plant model, the influence of different operation parameters on the overall plant performance has been investigated. Furthermore, introductory studies with the main objective to minimize the plant energy cost have been carried out. [Copyright &y& Elsevier]

The partial denitrification (PD) coupled with anaerobic ammonium oxidation (Anammox) (PD/A) process is a unique biological denitrification method for sewage that concurrently removes nitrate (NO 3 −-N) and ammonium (NH 4 +-N) in sewage. Comparing PD/A to conventional nitrification and denitrification technologies, noticeable improvements are shown in energy consumption, carbon source demand, sludge generation and emissions of greenhouse gasses. The PD is vital to obtaining nitrites (NO 2 −-N) in the Anammox process. This paper provided valuable insight by introduced the basic principles and characteristics of the process and then summarized the strengthening strategies. The functional microorganisms and microbial competition have been discussed in details, the S-dependent denitrification-anammox has been analyzed in this review paper. Important factors affecting the PD/A process were examined from different aspects, and finally, the paper pointed out the shortcomings of the coupling process in experimental research and engineering applications. Thus, this research provided insightful information for the PD/A process's optimization technique in later treating many types of real and nitrate-based wastewater. The review paper also provided the prospective economic and environmental position for the actual design implementation of the PD/A process in the years to come. [Display omitted] • PD/A was considered the most economically and environmentally beneficial new nitrogen removal process. • Principles and recent advances of the PD/A process are discussed. • The functional microorganisms and their competition in PD/A process are analyzed. • Influencing factors control and reinforcement strategies of PD/A was classified and conceptualized. • Knowledge gaps and prospects are presented to improve the future of PD/A process. [ABSTRACT FROM AUTHOR]

Minimising energy consumption in today's industrial sector is a crucial objective for achieving established climate goals. One effective strategy to enhance efficiency is optimising energy system operations within industries. The initial step in establishing operational optimisation involves developing a comprehensive model of the energy system. This model necessitates a specific structure to meet optimisation requirements. However, creating a model from scratch incurs substantial effort. While numerous companies possess energy models, they often lack the requisite structure for optimisation. Consequently, simplifying existing models can significantly reduce the effort needed to implement operational optimisation. This paper investigates the simplification of intricate industrial energy system models for optimisation purposes. The subsequent sections analyse two distinct approaches. One approach involves linearisation, while the other utilises neural networks. To facilitate a comparative analysis of these approaches, a reference model is developed. The assessment of these methodologies includes an investigation into optimisation robustness, computation time, accuracy concerning the reference model, and the effort required for developing and maintaining the simplified models. It proved that both approaches are suitable for operational optimisation. Linearisation exhibits superior computational efficiency compared to the neural network approach. The linearisation modelling approach together with the optimisation only required a few milliseconds for the calculation. The neural network approach needed 3 h for the calculation of the optimum with the genetic algorithm. The simulation of the neural network itself only required a few milliseconds. Hence, an improvement of the genetic algorithm is needed. However, the accuracy of linearisation falls short of that achieved by neural networks. The linearisation achieves a mean average percentage error from only 13%. In comparison the neural network's mean average error is 2.3%. Therefore, the linearisation must be improved. The impact using a piecewise linearisation on the results will be analysed in further research. [ABSTRACT FROM AUTHOR]

The application of a photovoltaic (PV) system for zero energy buildings is crucial to balance energy consumption. This paper investigated the potential of PV systems for buildings in different climate zones of China. The tilt angle, orientation, plot ratio, PV conversion efficiency and location were selected to study their influences on electricity production. The performance of the PV system was analyzed based on a residential building, and employed RETScreen in combination with TRNSYS. The results indicate that southwest China is the best place to develop zero energy buildings. Low-rise residential buildings can realize zero energy in China when the PV conversion efficiency is higher than 20%. However, an energy balance only occurs in the southwest when the PV conversion efficiency is 10%. For different building heights, with a high conversion efficiency (20%), the energy consumptions rates are less than 15 kW h/m2a all over China. In the southeast, the effect of the building shape on energy generation can be ignored due to the high solar elevation angle in the summer. Although the optimal tilt angle varies for different cities, the energy deviations are less than 10% when tilt angle is within 10° of the optimal angle. This study proposed a formula to evaluate the potential of a building PV system. The maximum difference between the simulated and calculated results is approximately 10%, which is reasonable and acceptable. This paper provides some references for the application of PV systems in zero energy buildings. [ABSTRACT FROM AUTHOR]

Due to the imperative nature of addressing climate change, the energy transition is currently underway, prompting the recognition of its urgency. Energy system optimisation models have emerged as crucial tools to assist policymakers in formulating laws and regulations that facilitate the transition towards carbon neutrality. While numerous models have been developed to explore various scenarios and define long-term objectives, only a few models focus on optimising the specific pathway to achieve these objectives. Many existing models lack the necessary time resolution to capture the integration of intermittent renewable energies; or are not open-source, creating a challenge in terms of transparency and reproducibility. This paper introduces EnergyScope Pathway, an open-source and documented model that addresses these limitations. It specifically optimises investment strategies for the whole-energy system over a 30-year period, or more, and optimising its hourly operation. This approach allows for a comprehensive evaluation of the effective integration of intermittent renewable energy sources. The model has a concise and efficient formulation, enabling its execution on personal laptops within approximately 15 min. By applying the model to the case study of Belgium, which presents challenges due to limited potential for renewable energy, we illustrate the importance of four pillars: energy efficiency, renewable energies, sector coupling, electrification and imports. The result pave the way to a new and incremental tool to support decision makers. In comparison to non open-source models, we verified the model's results with similar studies and found consistency in terms of technico-economic estimations. • Linear formulation of an optimisation model to represent a whole energy system transition pathway with an hourly resolution. • Short computational time, 15 min on a personal laptop • Quantify the pillars of the transition: efficiency, renewable energies and sector coupling. • Offers a new and incremental tool to support decision-makers in the energy transition. • Comparison of different approach to represent the transition. [ABSTRACT FROM AUTHOR]

• A mathematical model is proposed for tissue paper drying process. • The model realizes the accurate simulation of key parameters in the drying process. • The model provides global optimization scheme for key operating parameters. • The model improves the competitiveness by reducing 8.72% of the drying cost. The drying process accounts for the largest proportion of energy consumption in paper mills. Energy system optimization has a great significance for reducing the energy consumption of the paper drying process. The drying process is a complex system that consists of several subsystems, such as cylinder and air hood systems. Previous optimization models for energy systems usually focused on these subsystems. A global optimization methodology for the entire drying process is lacking, and no existing models can be applied in practice. In this work, an energy system optimization model for the tissue paper drying process is proposed based on a process simulation model. The modeling process integrates the various subsystems and fully considers the coupling of the paper drying process, which greatly enhances the industrial application value of the model. Industrial operating data are used to test the simulation model, and the results show that the simulation error of each key variable is within 5%, which meets the real-world production requirements and lays the foundation for an energy efficiency analysis of each subsystem. Applying the optimization model to a tissue paper mill, the results show that it can reduce drying costs by 8.71%. [ABSTRACT FROM AUTHOR]

Due to increasingly customised manufacturing, unpredictable ambient working conditions in shop floors and stricter requirements on sustainability, it is challenging to achieve energy efficient optimisation for machining processes. This paper presents a novel Cyber Physical System (CPS) and Big Data enabled machining optimisation system to address the above challenge. The innovations and characteristics of the system include the following four aspects: (1) a novel process of “scheduling, monitoring/learning, rescheduling” is designed to enhance system adaptability during manufacturing lifecycles; (2) an innovative energy model to support energy efficient optimisation over manufacturing lifecycles is developed. The energy model, which is enabled by CPS, Big Data analytics and intelligent learning algorithms, considers dynamic and aging conditions of machine tool systems during manufacturing lifecycles; (3) an effective evolutional algorithm based on Fruit Fly Optimisation (FFO), is applied to generate an adaptive energy efficient schedule, and improve schedule when there are significantly varying working conditions and adjustments on the schedule are necessary (that is rescheduling); (4) the system has been successfully deployed into European machining companies to verify capabilities. According to the results, around 40% energy saving and 30% productivity improvement have been achieved in the companies. A practical case study presented in this paper demonstrates the effectiveness and great potential of applicability of the system in practice. [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay’s theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice. [ABSTRACT FROM AUTHOR]

Electricity consumption and material wastage of manufacturing industries have directly or indirectly influenced the environment. Many manufacturing enterprises are committed to improving energy and material efficiencies. To achieve this goal, one of the high-efficiency ways is to optimize the scheduling. In this study, a multi-object optimization model is constructed for a particular kind of batch processing scheduling problem existed in flexible flow shops, where the objects include makespan, electricity consumption and material wastage. First, the models for electricity consumption and material wastage are built, based on which a mathematic model for scheduling problem is built afterwards. Finally, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) method is employed to solve the multi-object optimization scheduling model. A case study based on a real-world paper mill industry is presented to demonstrate the effectiveness of the method and its application in practice. Moreover, a real-life scheduling problem is investigated, and the result shows that the proposed method is more efficient than manual scheduling in energy and material saving. In fact, many tissue paper mills or other manufacturing industries schedule by manual labor in China, indicating that the proposed method has a significant potential in improving energy and material efficiencies in the studied flexible flow shop, and in energy and material saving in similar industries. [ABSTRACT FROM AUTHOR]

• A user-friendly Abaqus/CAE plug-in tool is developed to rapidly estimate crashworthiness by inputting geometry parameters of these multi-cell tubes. • The offset and zoom can optimize the crashworthiness of the 0th order hybrid tubes. • The crashworthiness of first-order hybrid tubes can be enhanced by self-similar inspired double space-filling. • COPRAS and discrete optimization method are used for design and optimization. Self-similar inspired multi-cell tubes have been widely used in energy absorbing devices due to their flexible and feasible design strategy. In this paper, a novel multi-cell hexagonal tube is constructed by space-filling and cross-section topological design of self-similar inspired multi-cell tubes. A user-friendly plug-in tool is developed in Abaqus/CAE to rapidly estimate the crashworthiness of these multi-cell tubes by taking as input the geometry of these multi-cell tubes. The discrete robust optimization algorithm and multi-object decision-making method (COPRAS) are used to select the optimal cross-section topology of the self-similar inspired multi-cell hexagonal tubes. In addition, the effect of different space-filling strategies and the number and layout of ribs on the crashworthiness of these multi-cell tubes are studied. The results show that the crashworthiness of these multi-cell structures can be further improved by double space-filling. Hence, these design strategies and optimization methods proposed in this paper can be used in the topology optimization of the multi-cell tubes. [ABSTRACT FROM AUTHOR]

Energy performance improvement in manufacturing is hindered by the lack of information on energy use in production and by the predominant economic vision in the assessment of energy interventions. This paper proposes a framework that integrates energy performance mapping with energy systems optimization. The framework introduces a new dimension to energy performance mapping by tracing energy use along processing and energy conversion steps and revisits multi-criteria assessments by assigning penalties to the energy performance gap with best practice. The suitability of food manufacturing for the application of the framework is exemplified from both a top-down and bottom-up perspective. More than 80% reductions in purchased energy carriers and non-renewable energy sources are achieved in a representative process through interventions coupling sector-specific manufacturing knowledge and energy expertise. With regard to one of the interventions, the introduction of the penalty is shown to shift the optimal solution, yielding more than 25% reductions in energy consumption compared to a purely economic assessment. The proposed framework can help industrial stakeholders identify improvement opportunities and develop more sustainable energy systems in manufacturing. Its widespread use can encourage poor performing companies to align with best practice and virtuous companies to continuous innovation to maintain their competitive advantage. [Display omitted] • A framework for energy performance improvement in manufacturing is proposed. • The framework introduces a new dimension to energy performance mapping. • The framework revisits multi-criteria assessments with an applicative orientation. • 80% reductions in purchased and non-renewable energy are achieved in a case study. • The introduction of the penalty yields 25% energy reduction in the optimal solution. [ABSTRACT FROM AUTHOR]

Utility systems must satisfy process energy and power demands under varying conditions. The system performance is decided by the system configuration and individual equipment operating load for boilers, gas turbines, steam turbines, condensers, and let down valves. Steam mains conditions in terms of steam pressures and steam superheating also play important roles on steam distribution in the system and power generation by steam expansion in steam turbines, and should be included in the system optimization. Uncertainties such as process steam power demand changes and electricity price fluctuations should be included in the system optimization to eliminate as much as possible the production loss caused by steam power deficits due to uncertainties. In this paper, uncertain factors are classified into time-based and probability-based uncertain factors, and operation scheduling containing multi-period equipment load sharing, redundant equipment start up, and electricity import to compensate for power deficits, have been presented to deal with the happens of uncertainties, and are formulated as a multi-period item and a recourse item in the optimization model. There are two case studies in this paper. One case illustrates the system design to determine system configuration, equipment selection, and system operation scheduling at the design stage to deal with uncertainties. The other case provides operational optimization scenarios for an existing system, especially when the steam superheating varies. The proposed method can provide practical guidance to system energy efficiency improvement. [ABSTRACT FROM AUTHOR]

This paper explores the use of data-driven approximation algorithms, often called surrogate modeling, in the early-stage design of structures. The use of surrogate models to rapidly evaluate design performance can lead to a more in-depth exploration of a design space and reduce computational time of optimization algorithms. While this approach has been widely developed and used in related disciplines such as aerospace engineering, there are few examples of its application in civil engineering. This paper focuses on the general use of surrogate modeling in the design of civil structures and examines six model types that span a wide range of characteristics. Original contributions include novel metrics and visualization techniques for understanding model error and a new robustness framework that accounts for variability in model comparison. These concepts are applied to a multi-objective case study of an airport terminal design that considers both structural material volume and operational energy consumption. [ABSTRACT FROM AUTHOR]

Use of biomass for energy purposes has many advantages, but it is not always profitable due to the characteristics of biomass. In order to improve the utilization of biomass it is necessary to create optimal conditions which require employment of several methods and technologies. This paper describes developed approach for consideration of options for the optimal utilization of available biomass potential under the current conditions (technological, economic, environmental and social). The developed approach is focused on support for decision making process on community level. Proposed approach includes mapping of biomass potential with defining both potential primary storage locations and potential locations of plant using geographic information system technologies. Also, it includes determining the optimal amount of biomass that can be justifiably used for energy purposes under current conditions by using the mathematical optimization with objective function related to maximizing net present value quotient. Four groups of technologies that can be used for biomass valorisation have been considered in the paper; biofuels production, thermal power plants, district heating and cogeneration. The proposed approach has been applied (as a case study) on Municipality of Ivanjica located in South-West region of Serbia. Municipality of Ivanjica is characterized by both significant forest area and significant forest biomass potential. There are 30 potential primary storages locations and 57 potential plants locations determined by geographic information system. However, only one of potential locations of plant and two of potential primary storage locations (6% of available potential) are determined by mathematical optimization as optimal and could be used under current conditions. [ABSTRACT FROM AUTHOR]

Due to its practicality, hybrid flowshop scheduling problem (HFSP) with productivity objective has been extensively explored. However, studies on HFSP considering green objective in distributed production environment are quite limited. Moreover, the current manufacturing mode is gradually evolving toward distributed co-production mode. Thus, this paper investigated a distributed hybrid flowshop scheduling problem (DHFSP) with objectives of minimization the makespan and total energy consumption (T E C). To address this problem, this paper designed a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG) by integrating the merits of genetic operator and iterated greedy heuristic. In this MOHIG, firstly, one cooperative initialization strategy is proposed to boost initial solutions' quality based on the previous experience and rules. Secondly, one knowledge-based multi-objective local search method is invented to enhance the exploitation capability according to characteristics of problem. Thirdly, an energy-saving technique is developed to decrease the idle energy consumption of machine tools. Furthermore, the effectiveness of each improvement component of MOHIG is assessed by three common indicators. Finally, the proposed MOHIG algorithm is compared with other multi-objective optimization algorithms, including SPEA2, MOEA/D, and NSGAII. Experimental results indicate that the proposed MOHIG outperforms its compared algorithms in solving this problem. In addition, this research can better guide practical production in some certain environments. • Considering green scheduling in distributed hybrid flowshop environment. • Designing a new energy-saving strategy into this problem. • Proposing a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG). • Evaluating performance of the proposed MOHIG by conducting comparison experiments. [ABSTRACT FROM AUTHOR]

Highlights • An approach for optimizing weighted matchings in wireless networks is proposed. • A distributed and asynchronous weighted matching algorithm is presented. • A tree-based efficient heuristic is provided for energy-efficient communication. • Time and message complexities are analyzed. • The effectiveness of proposed approach is shown by extensive experiments. Abstract Weighted graph matching is one of the most fundamental graph theoretical problems used in network design, especially in wireless network services such as radio resource allocation, physical layer security, energy-efficient partner selection and optimizing storage capacity. In this paper we present a distributed heuristic which provides nearly-optimal weighted matchings in polynomial time. We also propose an algorithm to decrease the number of transmitted messages to provide energy-efficient operation. Our approaches assume the asynchronous communication model and small messages having O (log (n)) bits. These assumptions directly fit the battery powered wireless networks such as wireless ad hoc and sensor networks. To the best of our knowledge, we propose the first distributed weighted matching algorithm which benefits from augmentation of augmenting paths having size larger than 3 for these networks. We also provide results of our simulations on synthetic geometric graphs to model wireless networks. Extensive simulations reveal that our algorithm improves the approximation performances of the other weighted matching algorithms and closes the gap between the approximation ratio and the optimum up to 32%. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the advanced exergy analysis was used to determine the avoidable and unavoidable exergy destructions of the heat exchanger networks (HENs) in a complex natural gas refinery in the South Pars gas field, focusing only on the improvement of the avoidable part. Exergy balances were evaluated for the HENs and the equations of advanced exergy destruction and exergetic efficiency for the HENs were developed. The total exergetic efficiency of the HEN in the plant was determined to be 62.8% that could be increased up to 84.2%, suggesting a high potential for improvement. Also, it was shown that the HEN1 had the most severe condition in the plant with the highest inefficiencies among the other networks. The avoidable and inevitable irreversibilities were calculated for all the heat exchangers running in the plant network. The advanced exergy analysis revealed that the exergy destruction had two major contributors. First, about 59% of the total irreversibility of the system was avoidable and could be eliminated by the well-known optimization techniques. Secondly, only 18 most inefficient heat exchangers (17% in numbers) contributed to more than 61% of the total exergy destruction in the plant network. Hence, there was a high potential for improvement of the operational cost in such a huge consuming energy system. Graphical abstract Image 1 Highlights • The HEN of a complex gas refinery has been analyzed by Advanced Exergy Analysis (AEA). • By AEA, exergy efficiency of the HEN was increased about 34% relative to the old one. • By AEA, the most inefficient heat exchangers in the network were found. • About 61% of total irreversibility of HEN corresponded to only 17% of the heat exchangers. • About 59% of the irreversibility of the HEN was avoidable and can be eliminated. [ABSTRACT FROM AUTHOR]

Abstract This paper aims to extend the operation time/range of an electric-powered multi-agent system (MAS) in leader-followertracking tasks, through integrating battery-based energy awareness with distributed tracking control synthesis. While MASs have gained much popularity nowadays, their use and deployment are often restricted by the operation time/range, due to the limited battery capacity. In an effort to overcome such a barrier, this work proposes to leverage a battery’s rate capacity effect to extend its runtime, which states that more energy can be drawn from the battery on less aggressive discharging rates. The battery-aware leader-follower tracking control design is then established in a model predictive control (MPC) framework, which strikes a tradeoff between tracking performance and energy consumption rates, accounts for the battery’s rate capacity dynamics, and incorporates the energy and power constraints. A distributed optimization method is used to distribute the MPC across the agents of the MAS. leader-follower tracking based on the proposed distributed MPC algorithm is then evaluated through a case study and compared with an existing algorithm in the literature. The simulation results show its effectiveness in extending the operation. Highlights • This is the first study of battery-aware leader-follower tracking control. • It uniquely leverages a battery’s dynamics to extend the tracking time/range. • A distributed model predictive control approach is designed. • Simulations validate the effectiveness and potential of the proposed results. [ABSTRACT FROM AUTHOR]

Economic and environmental impact of a combined cooling, heating and power (CCHP) system depends not only on its structure but also on the way it is operated. In this paper, a novel methodology which utilizes overall optimal partial loads of power generation unit (PGU) and novel performance curves (NPC) is proposed to optimize CCHP operation. The PGU overall optimum partial loads for demands below and above the CCHP operating curve are determined based on the optimization criteria and system characteristics. Proposed methodology is flexible and adaptable; it accounts for energy prices, carbon dioxide emissions, primary energy consumption factors and load variations with the weather conditions. Other strategies, such as following match performance, hybrid load, electric load, and thermal load strategies are shown to be the special cases of the NPC methodology. The performance of a CCHP system which operates based on the NPC methodology is compared to the CCHP performances when following match performance, hybrid load, electric load, and thermal load strategies. The comparison is carried out for two small hotel buildings in San Francisco and Miami and residential buildings in Dalian having different energy demand profiles. These locations have different energy prices, carbon dioxide emissions and primary energy consumption factors. The proposed methodology leads to the best operation when compared to other operating strategies based on operating cost, carbon dioxide emissions, primary energy consumption and a combination of them which is not always the case for other operating strategies. Proposed methodology provides a unifying framework which includes all previously operating strategies. [ABSTRACT FROM AUTHOR]

The Big Data driven approach has become a new trend for manufacturing optimisation. In this paper, an innovative Big Data enabled Intelligent Immune System (I 2 S) has been developed to monitor, analyse and optimise machining processes over lifecycles in order to achieve energy efficient manufacturing. There are two major functions in I 2 S: (1) an Artificial Neural Networks (ANNs)-based algorithm and statistical analysis tools are used to identify the abnormal electricity consumption patterns of manufactured components from monitored Big Data. An intelligent immune mechanism is devised to adapt to the condition changes and process dynamics of machining systems; (2) a re-scheduling algorithm is triggered if abnormal manufacturing conditions are detected thereby achieving multi-objective optimisation in terms of energy consumption and manufacturing performance. In this research, Computer Numerical Controlled (CNC) machining processes and industrial case studies have been used for system validation. The novelty of I 2 S is that Big Data analytics and intelligent immune mechanisms have been integrated systematically to achieve condition monitoring, analysis and energy efficient optimisation over manufacturing execution lifecycles. The applicability of the system has been validated by multiple industrial trials in European factories. Around 30% energy saving and over 50% productivity improvement have been achieved by adopting I 2 S in the factories. [ABSTRACT FROM AUTHOR]

A new indirect resolution method of an optimal control problem is proposed in this paper. And the optimization of the spacecraft low-thrust rendezvous with the fuel-minimum index to a safe region under the collision avoidance constraints is investigated. The objective is to minimize the fuel consumption in a power-limited low-thrust system, which leads to a bounded continuous control. The number of thrust arcs is unknown and the terminal positions in the rendezvous' safe region are unfixed for this optimization problem. The indirect resolution method of the optimal control employs deterministic interval analysis and gradient-based method to obtain the initial guess of the co-state variables. The interval analysis is used to sufficiently split, contract and clip the initial search space. And the gradient-based method is to determine the initial guess for each remained sub-space. Aiming at a low-thrust control system with the upper bound of acceleration of 5e −4 m/s 2 , numerical results are given to validate the proposed optimization method. [ABSTRACT FROM AUTHOR]

As the interest in energy storage units is increasing in the power sector, significant effort is being put into evaluating technological and business opportunities. However, many studies narrow their examinations and pursue a single goal, which may create false enthusiasm for energy storage. To tackle such issues, complex technical-economical case studies are necessary, with solid input data and real life applications, where the application of storage could generate revenue. In this paper, a recent study is presented, which aimed to examine the profitability of an energy storage unit, installed at an industrial or commercial consumer. The storage strategy was created through an optimization process considering two aspects: to decrease the power draw from the grid during peak hours and to decrease energy consumption of peak hours by shifting the needs to off-peak periods. A quasi-automatic decision support system was developed, which can determine parameters of the storage unit based on user defined inputs. Market potential of the idea was evaluated using data of a group of potential customers. Results have shown that the specified application can create positive net present value under certain circumstances. [ABSTRACT FROM AUTHOR]

Demand Response (DR) is one of the typical methods for optimizing load characteristics in power systems. Utilities offer DR schemes to generate incentives toward consumers’ power consumption behavior for load optimization. In tariff planning, power consumption variation is an important issue which is difficult to be analyzed quantifiably. This paper develops a boundary model for analyzing consumers’ power consumption behaviors, with a particular focus on residential home appliances. Candidate tariffs are analyzed in this model for their load variation potentials. Using three case studies, this paper reflects the potential for practical applications of the model on pricing and smart meter deployment. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a methodology for optimizing the operation of compressors in parallel in process industries. Compressors in parallel can be found in many applications for example in compressor stations conveying gas through long pipelines and in chemical plants in which compressors supply raw or processed materials to downstream processes. The current work presents an optimization framework for compressor stations which describe integration of a short term and a long term optimization approach. The short-term part of the framework suggests the best distribution of the load of the compressors (where the time scale is minutes) and the long-term optimization provides the scheduling of the compressors for large time periods (where the time scale is days). The paper focuses on the short-term optimization and presents a Real Time Optimization (RTO) framework which exploits process data in steady-state operation to develop regression models of compressors. An optimization model employs the updated steady-state models to estimate the best distribution of the load of the compressors to reduce power consumption and therefore operational costs. The paper demonstrates the application of the RTO to a network of parallel industrial multi-stage centrifugal compressors, part of a chemical process in BASF SE, Germany. The results from the RTO application showed a reduction in power consumption compared to operation with equal load split strategy. [ABSTRACT FROM AUTHOR]

This paper presents a review of the optimization techniques and strategies applied to wind turbine performance optimization. The topic is addressed by identifying the most significant objectives, targets and issues, as well as the optimization formulations, schemes and models available in the published literature. The current energy demand combined with depletion of fossil-fuel reserves and stricter environmental regulations have led to the development of alternative renewable energy solutions like wind energy. The current 2030 United States target is to have at least 20% of the US energy supply by onshore and offshore wind farms. To meet these demands, wind energy costs have to be able to compete with traditional fossil fuel sources. Hence, it is essential and vital that wind turbine designers and manufactures search the optimal solution that fits the objectives under a set of design constraints. Throughout the last 30 years, the objective function has evolved from the earlier maximized metric of the power coefficient to the maximization of the annual energy production. Common alternatives such as blade mass minimization and maximization of the rotor thrust and torque have been examined. However, the main objective has been focused on the minimization of the cost of energy in order for wind energy to become more competitive and economically attractive. The purpose of this paper is to review previous work that undertakes the performance optimization of horizontal wind turbines by highlighting the main aspects when tackling the wind turbine optimization problem such as: objective functions, design constraints, tools and models and optimization algorithms. In addition, in a conclusion of the review, a discussion and argument about the challenges, issues and future developments are identified. [ABSTRACT FROM AUTHOR]

Abstract: Alternative fueled engines offer greater challenges for engine control courtesy of uncertain fuel composition. This makes optimal tuning of input parameters like spark advance extremely difficult in most existing ECU architectures. This paper proposes the use of grey-box extremum seeking techniques to provide real-time optimization of the spark advance in alternative fueled engines. Since practical implementation of grey-box extremum seeking methods is typically done using digital technology, this paper takes advantage of emulation design methods to port the existing continuous-time grey-box extremum seeking methods to discrete-time frameworks. The ability and flexibility of the proposed discrete-time framework is demonstrated through simulations and in practical situation using a natural gas fueled engine. [Copyright &y& Elsevier]

The energy consumption in a multi-chiller system is very significant and its consumption will be increased by inappropriate operating of the chillers. Accordingly, the economic dispatch in multi-chiller systems becomes an important issue for saving energy. Such issue is known as optimal chiller loading, which is solved by applying different optimization methods. This study employs exchange market algorithm to obtain optimal set points of the economic dispatch problem on chiller plants, which is inspired by trading the shares on a stock market. Exchange market algorithm is proposed for solving continuous non-linear optimization problems and it is comprised of two searching and two absorbing operators. The decision variable of the optimal chiller loading is partial load ratio of each chiller and the objective function of the optimal chiller loading problem is minimizing the power consumption while satisfying the cooling demand. This paper adopts three case studies to evaluate the performance of exchange market algorithm and compares it with other optimization methods applied on the optimal chiller loading problem. Results show that exchange market algorithm is an efficient method for solving optimal chiller loading problem in chiller plants, and is adaptable for usage in air-conditioning systems. In addition, the comparison results show that the exchange market algorithm provides better solutions than previous optimization methods applied on the optimal chiller loading method in terms of convergence speed and total consumption of electrical energy. [ABSTRACT FROM AUTHOR]

We consider a novel multi-server queueing system that is potentially useful for optimizing real-world systems, in which the objectives of high performance and low power consumption are conflicting. The queueing model is formulated and investigated under the assumption that an arrival flow is defined by a batch Markovian arrival process and random values characterizing customer processing have the phase-type distribution. If the service time of some customer by a server exceeds a certain random bound, this server receives help from a so-called backup server from a finite pool of backup servers. The behavior of the system is described by a quite complicated multi-dimensional continuous-time Markov chain that is successfully analyzed in this paper. Examples of the potential use of the obtained results in managerial decisions are presented. [ABSTRACT FROM AUTHOR]

In this paper, a smart home energy management system (SHEMS) is developed using a limited memory algorithm for bound constrained problems known as L-BFGS-B, along with time-of-use (ToU) pricing to optimize appliance scheduling in a 24-h period. The allocation of energy resources for each appliance is coordinated by a smart controllable load (SCL) device embedded in the household's smart meter. SCL guarantees automation of the proposed SHEMS and prevents manual participation of customers in demand response (DR) programs. The model is simulated on a population of 247 residential prosumers with solar PV systems based on 15-min interval electric load data from a residential community in Austin, TX. After clustering households based on their electricity profiles, the proposed optimization model is performed. Simulation results show that the proposed autonomous scheduling system reduces cumulative energy consumption for customers across the different clusters. In addition, when households are grouped based on their respective category according to the ToU pricing scheme, the simulation reports a notable decrease in total energy consumption, from 65.771 kWh to 44.295 kWh, as well as a reduction in the cumulative cost of energy, from $6.550 to $4.393 per day. Simulation results confirm that the proposed algorithm effectively improves the operational efficiency of the distribution system, reduces power congestion at key times, and decreases electricity costs for prosumers. [ABSTRACT FROM AUTHOR]

The integration of multiple energy systems (MES) provides opportunities to explore the flexibility to accommodate more renewable energy. The concept and methodology of energy hub (EH) enable the standardized modeling of district MES. However, current EH formulations introduce nonlinearities into the modeling and present challenges to analysis and optimization. This paper proposes an automatic and linearized modeling method to formulate energy conversion in EHs, which simplifies the optimization of EH operations. On this basis, the flexibility of an EH is analyzed and quantitatively evaluated based on the ranks of the coupling matrices of the EH and its feasible operational region. Finally, an application of the linearized model on the interval optimization model is illustrated to show how it can suppress uncertainties and fluctuations in distributed renewable energy. A case study is used to demonstrate the effectiveness of the proposed model and the rationality of the flexibility analysis by comparing two EHs with different flexibilities. [ABSTRACT FROM AUTHOR]

This paper studies the optimization problem of energy efficiency in multi-cell networks with multiple users such as 5G network. However, the spectrum allocation and inter-cell interference (ICI) in multi-cell networks are the important challenges. The Soft Frequency Reuse (SFR) scheme which manages the spectrum and reduces ICI is used to build SFR-cellular networks. We take the global energy efficiency of the SFR-cellular network as the objective function of the optimal problem to obtain the maximum energy efficiency. Unfortunately, the objective function is a non-concave function, which is significantly difficult to be solved directly. Therefore, we utilize the fractional program, successive convex approximation, Lagrange dual, and Karush–Kuhn–Tucker (KKT) conditions to transform the objective function into a concave function. In such case, we could solve the problem with the convex optimization method. Finally, based on SFR, we propose a global energy efficiency optimization algorithm to search for the optimal energy efficiency of networks. Simulation results show that the proposed algorithm holds the better performance. [ABSTRACT FROM AUTHOR]

An urgent challenge in the manufacturing industry is increasing efficiency while decreasing energy consumption and environmental impact. Past studies addressing these issues have mainly focused on tool path optimization only considering machining efficiency. In this paper, we present a methodology to optimize the tool path for high efficiency, low energy consumption and carbon footprint in milling process. Firstly, the description and influencing factors of tool path are introduced. Then, a multi objective tool path optimization model with maximum machining efficiency, minimum energy consumption and carbon emission is proposed. Furthermore, the solution of the proposed model is introduced, which including two steps, one is the calculation of the number of cutter contact points (CCP), the other is using adaptive dynamic GA to optimize the connection sequence and ways of each CCP. Finally, the effectiveness and practicability of the method are verified by the machining experiments. [ABSTRACT FROM AUTHOR]

NC programs are widely developed and applied to various machining processes. However, the lack of effective NC program optimization strategy for the machining energy efficiency has been crippling the implementation of sustainability in companies. To address this issue, a multi-granularity NC program optimization approach for energy efficient machining has been developed and presented in this paper. This approach consists of two levels of granularities: the granularity of a group of NC programs for a setup where the features are machined on a single CNC machine with the same fixture and the granularity of a NC program. On the former level of granularity, the execution sequence of the NC programs for the setup of a part is optimized to reduce the energy consumed by the cutting tool change among the NC programs. On the latter level of granularity, the execution sequence of the features in the same NC program is optimized to reduce the energy consumed by the cutting tool's traveling among the machining features. Experiments on the practical cases show that the optimization results from this approach are promising and the approach has significant potential of applicability in practice. [ABSTRACT FROM AUTHOR]

With the increased complexity of air conditioning systems, the computational load and complexity of real-time optimization appear to be challenging for applications. To overcome the challenge, multiplexed optimization has been proposed in which multiple set-points are sequentially optimized and updated one by one. In such a way, it can achieve equivalently-good energy performance but with exponentially reduced computational load if compared with those traditional methods that optimize all set-points simultaneously. In fact, the time instant for each set-point optimization and the set-point optimization sequence have impacts on the overall performance of the multiplexed optimization. Considering such impacts, this paper proposes a new multiplexed optimization method for enhanced performance. In the proposed method, optimization is activated as the system energy efficiency is low. The set-point optimization sequence is selected according to their impacts on the system energy performance. The one with the most energy saving potential will be firstly optimized. The case study results demonstrate the improved performance of the proposed method in terms of energy, computation load and system stability when compared with the conventional multiplexed optimization. The proposed method can be applied in complex air conditioning systems for energy saving, computation load reduction and system stability improvement. [ABSTRACT FROM AUTHOR]

This paper presents a real-time optimization method and demonstrates its application to a Proton Exchange Membrane Fuel Cell (PEMFC) system. The optimization function was defined as mix of two performance indicators, the FC net power and the Fuel Consumption Efficiency, using the appropriate weighting coefficients. The weighting coefficients will modify the optimization surface and many extreme could appear on plateau of the optimization surface. The Global Extremum Seeking (GES) algorithm proposed here as real-time optimization method will locate and track the global maximum point and related to this will be established the optimal fueling rates for the PEMFC system under a given load. In this study four strategies will be tested, including the Static Feed-Forward (sFF) control strategy as reference. The optimal operating conditions were sought at different levels of load and the gaps between these four strategies were estimated. For example, in comparison with the PEMFC system controlled by the sFF strategy, the GES operation of the PEMFC system could increase the energy efficiency with 1–2.1%, depending on the FC current level and values used for the weighting coefficients. If the PEMFC system operates under variable load profile, the Fuel Consumption Efficiency could also increase with more than 0.54 W/lpm for GES&LF-based optimization strategy in comparison with the sFF strategy. The effectiveness of GES&LF-based optimization strategy was shown considering a FC Hybrid Power Source under variable load profile. [ABSTRACT FROM AUTHOR]

In the last years, multi-objective optimization techniques became into one of the main challenges of the building energy efficiency area. The objective of this paper is to develop and validate a computational code for multi-objective building performance optimization by linking an evolutionary algorithm and a building simulation software in a powerful cluster. A sophisticated version of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was implemented in Python code to determine the optimal building design, which allows working with categorical and discrete variables, and the objectives were evaluated using the building energy simulation software EnergyPlus. NSGA-II was implemented to run in a high-performance cluster for the parallel computing of the fitness of each population (set of possible designs). In this work, the strengths of the proposed method were demonstrated by its application to the optimal design of a typical single-family house, located in the Argentine Littoral region. This house has some rooms conditioned only by natural ventilation, and other rooms with natural ventilation supplemented by mechanical air-conditioning (hybrid ventilation). The most influential design variables like roof types, external and internal wall types, solar orientation, solar absorptance, size, type, and windows shading of this house among others were studied in two complex cases of 10 8 and 10 16 possibilities to obtain the best trade-off (Pareto front) between heating and cooling performance. Finally, a decision-making method was applied to select one configuration of the Pareto front. Optimal simulation results for the study cases indicated that is possible to improve up to 95% the thermal comfort in naturally ventilated rooms and up to 82% energy performance in air-conditioned rooms of the building with respect to the original configuration by using a design that takes simultaneous advantage of passive strategies like thermal inertia and natural ventilation. The methodology was proved to give a robust and powerful tool to design efficient dwellings reducing the optimization time from almost 12 days to 4.4 h. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions cause climate changes, and their impact on the environment continues to increase. As a result, there is an urgent need to accelerate efforts to reduce GHG emissions. In industry, the majority of the current methods of reducing GHG emissions depend on technical enhancements of the facility and equipment. These methods focus on local optimization for carbon emission reduction in enterprises and may require additional time, money and effort for satisfactory implementation. Unlike technical approaches that focus on the equipment, this paper proposes an approach that combines carbon footprint analysis and production planning. The carbon footprint of the entire company’s operational process can be analyzed systematically using this approach. Subsequently, carbon emissions can be reduced significantly through production planning and optimization. To test the effectiveness of the proposed approach, a pharmaceutical enterprise is employed as an example. A production planning model is constructed based on the energy consumption analysis of different units and equipment. Using this model, the carbon emissions of the enterprise can be analyzed, and the corresponding production plan can be developed. To determine the optimal solution, a hybrid discrete particle swarm algorithm is developed and tested based on real data collected from the pharmaceutical enterprise. The experimental results demonstrate that the proposed novel approach is effective and carbon emissions of the enterprise can be reduced by an average of 6.77%, or a reduction in CO 2 emissions of 610.2 tons per year. [ABSTRACT FROM AUTHOR]

In recent years there has been increased concern on energy efficiency of industries. Since the scheduling problems in the shop floors are directly related to the energy consumption, an appropriate way to improve energy efficiency in industrial plants is to develop effective scheduling strategies. Hence, the aim of this paper is to design an energy-efficient scheduling in a shop floor industrial environment, i.e., flexible job-shop scheduling problem (FJSP). To this end, a multi-objective optimization model is developed with three objective functions: (i) minimizing total completion time, (ii) maximizing the total availability of the system, and (iii) minimizing total energy cost of both production and maintenance operations in the FJSP. To cope with this multi-objective optimization problem, an enhanced evolutionary algorithm incorporated with the global criterion, as a multi-objective handling technique, is proposed and then performance evaluation is performed based on an extensive numerical analysis. [ABSTRACT FROM AUTHOR]

Belt conveyors and crushers are always assembled in series to form coal conveying systems; reasonably, this paper takes them as a whole for energy efficiency optimization. The energy models of the key energy consuming devices, belt conveyors and crushers, are firstly constructed. They are then employed to formulate an open loop energy efficiency optimization problem for the studied coal conveying systems. The coal feed rate, belt speed and crusher rotational speed are taken as the optimization variables; and, the energy cost, with consideration of time-of-use (TOU) tariff, is formulated as the objective function. Next, basing on the above open loop optimization problem, a closed-loop model predictive control (MPC) strategy is constructed. The MPC strategy has the ability to deal with various disturbances with its feedback correction and receding horizon optimization mechanisms. A coal conveying system in a coal-fired power plant is taken as a case study for verification of the two strategies. The open loop optimization and MPC strategies are investigated respectively for comparison studies. The results show that, unlike the open loop optimization, the MPC strategy can deal with the disturbances of coal consumption forecasting, the disturbances of belt feeding rate and the disturbances of mean particle size of feeding coal effectively. The MPC strategy can considerably improve the energy efficiency of the whole coal conveying system while satisfying all the constraints. Its robustness and adaptability are verified through the comparison studies. [ABSTRACT FROM AUTHOR]

The production of stainless steel begins by melting of scrap in melt shop that is known as one of the most electricity intensive processes. Volatile and uncertain renewable energy sources cause high fluctuations in electricity prices and thus usually higher costs for the final user such as a melt shop. This paper presents an integrated production and electricity optimization system that can help the melt shop to adjust its production schedule to volatile electricity prices and thus lower its costs. An existing production scheduling optimization system based on a continuous-time mixed-integer linear programming model has been further developed to account for electricity costs as well. It deploys an intelligent heuristics which decomposes the overall optimization problem into several sub-problems of smaller size in order to achieve faster and more robust solution. The electricity-aware optimization system has been successfully tested and implemented in a melt shop where it managed to reduce electricity costs by around 3%. At the same time, the system has improved coordination between different production stages, and thus made the entire melt shop more flexible, agile and responsive to unexpected events. The system has also been recognized as a very useful tool for running various simulations, what-if analysis and business scenarios on the melt shop in order to identify bottlenecks and further increase its production rate. [ABSTRACT FROM AUTHOR]

Multi-hop D2D communication has been proposed with the purpose of improving the coverage, quality of service (QoS), and flexibility and adaptability of single-hop D2D communication. However, multi-hop D2D communication often experiences obstacles caused by interference from the shared channel, which makes spectrum efficiency in multi-hop D2D networks an important issue to tackle. In this paper, we study the optimization of spectrum efficiency in multi-hop D2D communication underlaying cellular networks. First, we use iteration-based optimization techniques such as exhaustive search (ES) and gradient search (GS) with barrier function to find the global and local optimal solutions, respectively. More importantly, we propose two machine learning (ML) techniques, the unsupervised deep neural network (DNN) and deep Q-learning (DQL) algorithms and evaluate the performances of both algorithms compared to iteration-based optimization methods. The simulation results verify that both algorithms achieve near-global optimums compared to GS. Moreover, it is verified that the DQL outperforms the unsupervised DNN in terms of optimal spectrum efficiency, while the DQL algorithm has higher time complexity than the unsupervised DNN. • Evaluate energy efficiency of wireless 'multi-hop' D2D underlay cellular networks. • Build an optimization model for energy efficiency under given environment. • Design unsupervised DNN and deep Q-learning to solve the optimization model. • ML-based algorithms achieve near-global optimal solutions with lower time complexity. [ABSTRACT FROM AUTHOR]

This paper studies a novel guidance framework of the vehicle against a high-speed and maneuvering target based on deep reinforcement learning (DRL) considering the energy consumption, autopilot lag dynamics, and input saturation, which can effectively cope with the high flight-path angle error flight phase and various uncertainties. The guidance framework proposes an end-to-end mapping transformation between the guidance command and observation states consisting of line-of-sight (LOS) angle, relative distance, and their rate measured by the seeker. At the same time, the observability of the LOS angle and relative distance is included in constructing the reward function. Besides, the relative engagement kinematic model of the interceptor-target is established and combined with the PPO guidance algorithm, jointly described as a Markov decision process (MDP). Notably, the guidance framework is optimized using the improved proximal policy optimization (PPO) algorithm and demonstrated in a simulated terminal phase in the near-space. Specifically, the PPO guidance algorithm is structured by the policy (actor) neural network and the critic neural network, and both are standard fully-connected neural networks. Subsequently, observation states and rewards are fully collected and applied by introducing the experience replay method. Also, the exponential decay learning rate method, mini-batch stochastic gradient ascent (SGA) method, zero-score standardization, and Adam optimizer are proposed to train the reinforcement learning algorithm more efficiently. Moreover, the proposed guidance framework has an excellent generalization capability and guarantees the implementation of fixed and stochastic engagement scenarios, which means that the interceptor can realize the unlearned practical combat scenarios. The robust capacity is indicated and validated using Monte Carlo simulation under various uncertainties. Moreover, the DRL guidance framework can satisfy the onboard application requirement. [ABSTRACT FROM AUTHOR]

Abstract: There are many different aspects a production-planning model has to be able to handle to make a model adequate for the purpose. One aspect is the handling of start-ups and shutdowns for different processes. The production plan is likely to be changed when considering, for example, a cost connected to the start-up and/or shutdown of processes. Besides costs associated with start-ups and shutdowns, waste may be produced during the start-up and shutdown. However, there is also the possibility of carrying out soft start-ups and shutdowns or limiting the number of start-ups and shutdowns. Thus, start-ups and shutdowns have to be handled in an adequate way in models to produce reliable and accurate results. In optimisation tools, this may be dealt with by introducing certain constraints, including integers. In this paper, the implementation of alternative ways to consider start-ups and shutdowns are presented. This is done in the energy system optimisation tool reMIND, which deals with Mixed Integer Linear Programming (MILP) problems. The purpose of this paper is to show four alternatives to consider start-ups and shutdowns in optimisation models. This involves, in total, almost 50 constraints. Also, a simple dairy case study is included in the paper to visualise the effect of implementing the different alternatives to shutdowns. [Copyright &y& Elsevier]

Natural gas is usually transported by pipeline networks. This paper presents a multi-objective optimization model in optimizing the operation of natural gas pipeline networks. A mathematical model is established for different network configurations, namely linear, branch and looped topologies. Two conflicting objectives, power consumption minimization and gas delivery flow rate maximization, are considered subjected to pipeline and compressor constraints. The decision variables are the pressure at the nodes and the rotational speed of the compressors. A set of constraints is developed based on gas flow equations and compressors operating conditions. Due to the nonlinearity of the constraints and the objective, the developed model is a Nonlinear Programming problem. The optimization of the models is performed with NSGA-II algorithm. The solution obtained is a set of Pareto optimal points from which a decision maker can select a specific preferred solution. Sensitivity analysis was performed to determine the impact of parameter changes. [ABSTRACT FROM AUTHOR]

This paper proposes a well-suited strategy for High Performance Computing (HPC) of density-based topology optimization using Graphics Processing Units (GPUs). Such a strategy takes advantage of Massively Parallel Processing (MPP) architectures to overcome the computationally demanding procedures of density-based topology design, both in terms of memory consumption and processing time. This is done exploiting data locality and minimizing both memory consumption and data transfers. The proposed GPU instance makes use of different granularities for the topology optimization pipeline, which are selected to properly balance the workload between the threads exploiting the parallelization potential of massively parallel architectures. The performance of the fine-grained GPU instance of the solving stage is evaluated using two preconditioning techniques. The proposal is also compared with the classical CPU implementation for diverse topology optimization problems, including stiffness maximization, heat sink design and compliant mechanism design. [ABSTRACT FROM AUTHOR]

Landing safety is the prior concern in planetary exploration missions. With the development of precise landing technology, future missions require vehicles to land on places of great scientific interest which are usually surrounded by rocks and craters. In order to perform a safe landing, the vehicle should be capable of detecting hazards, estimating its fuel consumption as well as touchdown performance, and locating a safe spot to land. The landing site selection process can be treated as an optimization problem which, however, cannot be efficiently solved through traditional optimization methods due to its complexity. Hence, the paper proposes a synthetic landing area assessment criterion, safety index, as a solution of the problem, which selects the best landing site by assessing terrain safety, fuel consumption and touchdown performance during descent. The computation effort is cut down after reducing the selection scope and the optimal landing site is found through a quick one-dimensional search. A typical example based on the Mars Science Laboratory mission is simulated to demonstrate the capability of the method. It is proved that the proposed strategy manages to pick out a safe landing site for the mission effectively. The safety index can be applied in various planetary descent phases and provides reference for future mission designs. [ABSTRACT FROM AUTHOR]

The electric engine cooling system, where the coolant pump and the radiator fan are driven by electric motors, admits advanced control methods to decrease auxiliary energy consumption. Recent publications show the fuel saving potential of optimal control strategies for the electric cooling system through offline simulations. These strategies often assume full knowledge of the drive cycle and compute the optimal control sequence by expensive global optimization methods. In reality, the full drive cycle is unknown during driving and global optimization not directly applicable on resource-constrained truck electronic control units. This paper reports state-of-the-art engineering achievements of exploiting vehicular onboard prediction for a limited time horizon and minimizing the auxiliary energy consumption of the electric cooling system through real-time optimization. The prediction and optimization are integrated into a model predictive controller (MPC), which is implemented on a dSPACE MicroAutoBox and tested on a truck on a public road. Systematic simulations show that the new method reduces fuel consumption of a 40-tonne truck by 0.36% and a 60-tonne truck by 0.69% in a real drive cycle compared to a base-line controller. The reductions on auxiliary fuel consumption for the 40-tonne and 60-tonne trucks are about 26% and 38%, respectively. Truck experiments validate the consistency between simulations and experiments and confirm the real-time feasibility of the MPC controller. [ABSTRACT FROM AUTHOR]

In a central heating, ventilation, and air conditioning (HVAC) system, the set-points for several local control loops have a significant influence on the overall energy performance of the system. Real-time optimization (RtOpt) of those set-points has therefore been widely studied. However, due to the nonlinear dynamics of the HVAC system as well as the constraints associated with the system operation, real-time optimization always suffers from a heavy on-line computational load when those set-points are optimized simultaneously. To overcome this problem, multiplexed real-time optimization (MRtOpt) has been developed, which optimizes only one set-point at a time but with a faster optimization frequency. Because frequently resetting the set-points introduces artificial disturbances into the local control loops and may deteriorate the system stability, this paper presents a study to enhance the system stability of the multiplexed real-time optimization by integrating a degree of freedom (DOF) based set-point reset to renew the set-points instead of the conventional step-change set-point reset. The control performance of the integrated strategy was investigated using case studies. The results showed that around 10% of the energy saving was achieved by the proposed method compared with a method without real-time optimization. When compared with the conventional real-time optimization method, the proposed method resulted in around 70% computational load reduction, and over 26% reduction in the tracking errors of the local control loops. [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]