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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 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]

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]

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]

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]

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]

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]

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]

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]

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]

Abstract: Machine tools are responsible for environmental impacts owing to their energy consumption. Cutting parameters have been optimized to minimize cutting power, power consumed or cutting energy. However, these response variables do not consider the energy demand that ensures the readiness of the machine tool. The present paper outlines an experimental study to optimize cutting parameters during turning of AISI 6061 T6 under roughing conditions in order to get the minimum energy consumption. An orthogonal array, signal to noise (S/N) ratio and analysis of variance (ANOVA) were employed to analyze the effects and contributions of depth of cut, feed rate and cutting speed on the response variable. A comparison was done to highlight the importance of correctly selecting the response variable to be analyzed, due to the difference of the values of cutting parameters needed to optimize cutting power, cutting energy, power consumed and energy consumed during the machining process. Additional, the relationship between cutting parameters, energy consumption, and surface roughness was analyzed in order to determine the levels of the cutting parameters that lead to minimum energy consumption and minimum surface roughness. The results of this research work showed that feed rate is the most significant factor for minimizing energy consumption and surface roughness. Nevertheless, the level of this factor needed to achieve minimum energy consumption is not the same as the one needed to obtain minimum surface roughness. Higher feed rate provides minimum energy consumption but will lead to higher surface roughness. [Copyright &y& Elsevier]

Abstract: Large forming presses require great amounts of construction metal materials. Nevertheless, eco-design principles in the field of forming presses have seldom been confronted in the scientific literature. In this paper, an optimization model is proposed, suitable for designing press frames which are optimal in terms of energy efficiency. First, a framework is proposed for modular and functional description of a machine tool is described, in order to identify the largest energy consuming modules and functions. Then, a simple analytical model of loads, stresses and deformations is proposed for pre-stressed structures. Then, the equations of the analytical models are used as the constraints of a numerical global optimization algorithm, aimed at minimizing the amount of energy stored into the press frame and the extra-energy due to deformation of the columns in the usage lifetime of the press. The results clearly show that, only if the press frame structure is monolithic, it is possible to obtain a solution which is truly optimal. This conclusion is robust with respect to potential noise or uncertainty issues, which in this case are mainly related to the coefficient of the objective functions. [Copyright &y& Elsevier]

Abstract: Reducing the energy consumption during machining of metal matrix composites (MMC) can significantly improve the environmental performance of manufacturing systems. To achieve this, calculation of energy consumption in the computerized numerical controlled (CNC) turning machine is required. It is important to minimize the power consumption and maximize tool life during machining operations, being performed on the CNC turning machine. However, this is challenging due to complexity of manufacturing systems and the nature of material being machined. This paper presents the findings of experimental investigations into the effects of cutting speed, feed rate, depth of cut and nose radius in CNC turning of 7075 Al alloy 15 wt% SiC (particle size 20–40 μm) composite. Design of experiment techniques, i.e. response surface methodology (RSM) has been used to accomplish the objective of the experimental study. The machining parameters such as cutting speed, feed rate, depth of cut and nose radius are optimized by multi-response considerations namely power consumption and tool life. A composite desirability value is obtained for the multi-responses using individual desirability values from the desirability function analysis. Based on composite desirability value, the optimum levels of parameters have been identified, and significant contribution of parameters is determined by analysis of variance. Confirmation test is also conducted to validate the test result. It is clearly shown that the multi-responses in the machining process are improved through this approach. Thus, the application of desirability function analysis in response surface methodology proves to be an effective tool for optimizing the machining parameters of 7075 Al alloy 15 wt% SiC (20–40 μm) composite. Result of this research work show that when turning is be carried out at values of machining parameters obtained by multi response optimization through desirability analysis route this will reduce power consumption by13.55% and increase tool life by 22.12%. [Copyright &y& Elsevier]