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Iron & Steel, Pulp & Paper, Non-Metallic Minerals and Chemical & Petrochemical are the most energy intensive subsectors, even though they utilise only a limited range of production processes compared to other sectors like Machinery or Food & Beverages. To support future efforts for decarbonising the European industry, this study aims to develop a methodology to correctly and dynamically depict all relevant production processes of the mentioned subsectors and to generate synthetic load profiles (LP) 1 1 LP ... Load Profile. based upon their consumption and generation behaviour. In a first step, the energy intensive subsectors and their main production processes are identified. A standardised research approach is used to correctly depict their characteristics e.g. runtime, energy consumption and generation, unit sizes etc. Next, a methodology for modelling the timely behaviour of these production processes and for generating synthetic LPs is developed. This method is based upon the bottom-up approach of discrete-event simulation combined with stochastics. The developed methodology is then implemented into the simulation software Ganymed. Finally, the results of this methodology are validated via a case study, modelling the primary steel production route of an Austrian steel mill. In overall, the synthetic electricity LP shows good approximations to the measured one with a mean absolute percentage error of 6.08% for the simulated five days in total. However, a stronger deviation of the generated LP compared to the measured counterpart can be noted at the last two days. This deviation results from a reduction of the capacity during the real life production. This, however, can be taken into account in the synthetic generation given a more extensive data basis. Consequently, Ganymed can be deemed as a suitable software for generating energy consumption and generation behaviour of processes and production chains of energy intensive industries. • Production processes of energy intensive industries are classified and structured. • A methodology for generating load profiles of these industries is presented. • Discrete-event simulation is enhanced extensively to accurately depict industries. • Load profiles of various energy carriers for these industries can be generated. • Various system dimensions from single process to overall plant can be depicted. [ABSTRACT FROM AUTHOR]

We present a mechanics based model for predicting the power consumption of drilling operations. Different from existing power models in machining that ignore the tool wear, our model takes into full consideration the tool wear which is particularly pronounced in drilling and causes extra power consumption. For any given spindle speed n and feed rate f , our model establishes the relationship between the length of drill and the total power consumption as well as the amount of tool wear. With this prediction model established, we can then optimize the drilling parameters (n , f) towards different objectives, such as the two applications reported in this paper – to minimize the average power consumption per unit length of drill and to maximize the tool usage before its replacement. Physical drilling experiments of the proposed power prediction model and its two optimization applications are also reported in this paper which have validated the accuracy of the model and convincingly demonstrated its efficacy in deciding optimal drilling parameters (n , f) for energy minimization and other objectives. • An energy consumption model (ECM) for drilling process considering the tool wear is proposed. • The proposed ECM is more accurate and suitable for actual processing and production. • The proposed ECM can conveniently and effectively predict tool life and monitor tool wear. • Optimization of cutting parameters to achieve maximum tool life and minimum energy consumption. [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]

This paper presents a simple computational model for determining whether additive manufacturing or subtractive manufacturing is more energy efficient for production of a given metallic part. The key discriminating variable is the fraction of the bounding envelope that contains material – i.e. the volume fraction of solid material. For both the additive process and the subtractive process the total energy associated with the production of a part is defined in terms of the volume fraction of that part. The critical volume fraction is that for which the energy consumed by subtractive manufacturing equals the energy consumed by additive manufacturing. For volume fractions less than the critical value, additive manufacturing is more energy efficient. For volume fractions greater than the critical value, subtractive manufacturing is more efficient. The model considers the entire manufacturing lifecycle – from production and transport of feedstock material through processing to return of post-production scrap for recycling. Energy consumed by processing equipment while idle is also accounted for in the model. Although the individual energy components in the model are identified and accounted for in the expressions for additive and subtractive manufacturing, values for many of these components may not be currently available. Energy values for some materials' production and subtractive and additive manufacturing processes can be found in the literature. However, since many of these data are reported for a very specific application, it may be difficult, if not impossible, to reliably apply this data to new process-material manufacturing scenarios since, very often, insufficient information is provided to enable extrapolation to broader use. Consequently, this paper also highlights the need to develop improved knowledge of the energy embodied in each phase of the manufacturing process. To be most valuable, users of the model should determine the energy consumed by their manufacturing process equipment on the basis of energy-per-unit-volume of production for each material of interest – considering both alloy composition and form. Energy consumed during machine idle per unit time should also be determined by the user then scaled to specific processing scenarios. Energy required to generate feedstock material (billet, plate, bar, wire, powder) must be obtained from suppliers. [ABSTRACT FROM AUTHOR]

The low price and excellent flexibility of plastic straws make them very popular, yet their short lifespan and improper disposal after use result in major resource waste and environmental burdens. To this end, paper straws and polylactic acid (PLA) straw alternatives have emerged on the market. Aiming to present a comparative sustainability study of drinking straws, this article provides a life cycle assessment of drinking straws made of polypropylene (PP), paper, and polylactic acid (PLA) based on Chinese context, as well as a scenario analysis of the availability of low-carbon electricity (LCE) and its recycling capacity (RC). The functional unit was set as a straw suitable for 500 ml milk tea cups, and the system boundary is from cradle to grave, including raw material preparation process, straw production process, usage process, waste process, transportation and recovery process. The comparative analysis revealed that the plastic straw alternative would not be superior in all indicators, but would lead to environmental problem-shifting, which will provide a basis for decision making and avoid bias caused by subjective choices. Scenario analysis shows that the synergistic effect of LCE and RC can minimize the environmental burden for paper straw, especially in terms of Primary Energy Demand (PED) which reduced by 27.9% with 100% RC and 50% LCE, and this is mainly due to RC. The findings of this work can offer theoretical justification, data references, and fresh perspectives for environmental evaluation of straws. [Display omitted] [ABSTRACT FROM AUTHOR]

Significant increases in industry energy efficiency are crucial for the transformation of the world's energy systems. Many production sites offer high energy savings capabilities, and if these are accompanied by short periods of economic amortization, companies should be willing to act. This case study provides a novel and extended energy assessment for plastics processing plants including primary energy, greenhouse gas emissions, and energy costs. The research distinguishes between the standard form of separate individual energy assessments and provides a more innovative holistic approach taking all relevant energy flows within the production system into account. Dynamic simulation offers a quick and effective way to predict the results of the possible energy saving measures highlighted in this analysis. The paper presents validated energy consumption simulations based on realistic processing conditions for two injection molding factories in different climatic zones. The results show that combining a number of separate energy saving measures can reduce the primary energy demand by around 26% for a German plant under temperate climate conditions and 20% for a Western Australian plant under Mediterranean conditions. However, when the separate energy saving measures are holistically combined the reduction in energy use significantly increases to 41% and 43% respectively. This holistic energy strategy involves incorporating better cogeneration and waste heat recovery options. For small and medium sized companies in particular major energy infrastructure investments may often be considered too expensive without examining the extended benefits from a holistic energy assessment perspective. In contrast, a holistic framework, like the one suggested in this paper could provide a number of new options for increasing energy efficiency that individually might normally not be accepted under conventional economic rate of return analysis. [ABSTRACT FROM AUTHOR]

Cleaner production is a green production way to minimize emissions and waste as well as maximize product output. Cleaner production has proven itself as an effective way to reduce energy consumption and improve material utilization during the whole manufacturing process. However, the implementation of cleaner production strategy is facing barriers due to the lack of applying advanced technologies such as cloud manufacturing, Internet of Things, and cyber-physical system. Based on these advanced technologies, this paper presents an architecture of energy-cyber-physical system enabled management for energy-intensive manufacturing industries to promote the implementation of cleaner production strategy. An energy-cyber-physical system enabled green manufacturing model for the future smart factory is proposed. Then the qualitative and quantitative synergetic models based on energy-cyber-physical system are developed for cleaner manufacturing. Finally, an application of a partner company is presented to demonstrate the proposed framework and synergistic models. The results show that energy consumption can be greatly reduced using synergistic evaluation models. Further managerial implications are summarized to increase the level of synergy of energy-cyber-physical system, which can achieve cleaner production strategy by making energy-efficient decisions from different departments. • An architecture of energy-CPS enabled management is proposed to save energy. • Energy-CPS enabled intelligent model is proposed for green manufacturing. • Qualitative and quantitative synergetic models are developed for cleaner production. • The energy consumption can be reduced through the synergistic evaluation models. [ABSTRACT FROM AUTHOR]

This paper presents the application of the Discrete Event Simulation (DES) in an industrial plant to analyze the behavior of the consumption and generation of electrical energy in conjunction with other variables of the process. In other words, the process modeling was conducted considering the energy consumption in equipment as well as the local photovoltaic (PV) energy generation. A medium-sized plant was the reference base used herein, and the main variables considered were the time of supply of tanks and processing of raw material, active power of equipment and energy tariffs. A simulation model with resolution in seconds was designed, enabling the simulation of consumption curves of each equipment of a given part of the production process. The equipment of variable active demand was modeled by a mathematical regression method, and the others were assumed with constant power. The same model was altered to unit in minutes for simulating the monthly electricity consumption, so as to obtain financial results. The contractual rules for electricity supply were included, and the use of a diesel-powered generator was taken into account. The application of a freeware was employed as a low cost alternative for small and medium-sized companies, even though the lack of graphic resources has increased the complexity of the system modeling process. The model with resolution in seconds proved to be efficient to simulate the consumption profile of a specific equipment that alters the power in a short-time period, such as the pumps. The model, on the other hand, in minutes allowed a more effective analysis in terms of finance, enabling the verification of alternative scenarios throughout a month. The monthly cost reduction perceived by considering self-generation in this model was about 36%. Detailed diagram of the main elements of modeling of a manufacturing process of an industry concentrating on the consumption and generation of electric energy. Image 1 • The electricity consumption in an industrial process can be approached in a simulation model. • PV energy can be simulated together with variables of industrial processes. • The discrete event simulation can be used in industrial processes to optimize the electricity management. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has been recognized as a promising technology for achieving more secure and resilient supply chains to support social transitions to sustainable post-fossil carbon societies. AM is considered to be efficient and sustainable and its environmental impact is mainly from energy consumption. Therefore, it is of great significance to accurate predict and thus reduce the energy consumption of AM. However, current models to predict AM energy consumption are not sufficiently accurate. In this paper, a hybrid mechanism-based and data-driven approach is proposed to forecast the AM energy consumption. This approach accurately modelled the working time and power of each energy-consuming component of the FDM machine. Firstly, the physical model was used to establish the energy consumption prediction framework which documents the total energy into energy consumed by each component. Secondly, the data-driven method was leveraged via data fitting and deep learning to obtain the power and temporal model of the nozzle and the platform, considering their working states and interactions between them and the cooling fan. Thirdly, experimental measurements and G-code parsing were utilized to obtain data of the power model and the temporal model of the remaining components. In this work, the different types of parts were fabricated by the fused deposition modelling (FDM) printer and the energy consumption was measured and compared with the predicted values to verify the accuracy and effectiveness of the proposed method. Finally, the proposed method was compared with other methods to document the benefits of the proposed method. The results showed that the prediction accuracy of the proposed method for these four experiments was more than 94% accurate and it outperformed other methods. The proposed method can be used to guide process design to more accurately predict and thus reduce the energy consumption of AM parts during their manufacturing processes. [ABSTRACT FROM AUTHOR]

Construction is nowadays considered an extremely energy intensive industry and one of the main sources of environmental pollution in the world. Therefore, the research and the development of novel energy-saving manufacturing processes and sustainable construction materials is more than ever urgent and challenging. This paper aims at identifying the industrial process for the production of novel geopolymers to be used as a greener substitute for cement, especially the Portland one, widely used for structural applications to significantly reduce the environmental impact of the construction industry. Here, the materials sustainability and the manufacturing process are improved by valorising and reusing wastes, deriving from the pulp-paper industry, as raw materials. This industry generates a great quantity of wastes that represent a real concern both for their environmental impact and cost of disposal. The new industrial process was evaluated after scaling up the laboratory procedure to an industrial dimension. Consequently, each small-scale process was deeply analysed (raw materials involved and processes) and transposed into a massive-production. The most relevant processes that show an elevated environmental impact were identified and the energy consumption of the whole process has been assessed resulting, for the analysed geopolymeric product, an embodied energy of 645 MJ/ton, being about one sixth of the OPC. Moreover, a preliminary economic analysis has been conducted in terms of raw materials involved in the proposed industrial process revealing a decreasing cost per ton increasing the waste recycling. All considered, the main conclusions are that the industrial scale up of the proposed geopolymeric product, assessed for the first time in literature along with the related proposed considerations, could be highly competitive to the Portland production involving greener processes with a much lower energy consumption and a greater financial saving by improving the wastes quantity. [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]

The material processing industry has to deal with material and energy reduction targets, and such a need becomes more and more urgent when future energy and resource consumption predictions are considered. The optimization of machining operations has conventionally been performed by identifying the range of process parameters that lead to the maximum efficiency, which are generally defined on the basis of maximum productivity and minimum cost criteria. Cost-based models are part of the industrial cultural background, while the emerging environmental impact analysis has not yet become fully established. The paper describes an approach that is aimed at the integration of system-level models for the environmental and economic assessment of machining. Process time, cost, energy demand and carbon dioxide emissions are assumed as optimization targets. The dry and wet turning of Ti-48Al-2Cr-2Nb and Ti-6Al-4V alloys, while varying the process parameters, is considered as a case study. The results highlight that the range of process parameters that allow the maximum process efficiency to be achieved is influenced by material machinability, and that a holistic approach to process optimization appears to be a key task which is worth further research efforts. [ABSTRACT FROM AUTHOR]

Dry and wet routes in the ceramic tile manufacturing process refer to two different technologies for preparing the raw materials for the forming stage. Both result in a granulated solid ready for use in the pressing stage, but with different characteristics. The dry route was the first to be developed. As quality standards and tile sizes increased, the wet route was developed and introduced successfully into the manufacturing process. Since 1990, the wet route has been the most used around the world to prepare ceramic tile body raw materials. The powder produced by the wet route has finer particles and higher flowability, which has allowed the production of higher quality ceramic tiles of larger sizes. However, the process uses more energy and water and, consequently, is more costly in both economic and environmental terms. New developments in dry milling and granulation systems combined with the growing awareness of environmental impacts and European Union energy policy, and successful uses of the dry route in some important tile producer countries (especially Brazil), are leading to a reconsideration of the tile production processes. In the current context, implementation of the dry route is being considered an interesting alternative for the production of ceramic tiles with lower environmental costs. This has resulted in a significant number of studies of the technology from applied research centres and machinery producers. The present paper provides an up to date technical and environmental comparison of the dry and wet routes, based on the most recent advances, to add to the debate on the use of the dry route to produce high quality ceramic tiles. [ABSTRACT FROM AUTHOR]

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

Optimizing energy consumption has become a priority in advanced manufacturing industry due to increasing sustainability and environmental consciousness, resulting in various models to characterize the relationship between cutting parameters and energy consumption for material removal processes. These existing models are subject to a principle, the higher the material removal rate, the lower the specific energy consumption, without considering the effects of cutting parameter combinations at a certain material removal rate. To address this problem in minimizing energy consumption, three tasks are conducted in this paper. First, specific energy consumption is identified as an optimization objective and expressed as a function of spindle rotation speed, cutting speed, feed rate and depth of cut. Second, based on the candidate options, decision rules are proposed for selecting optimal cutting parameters to achieve minimum energy consumption. Third, the decision rules are applied with experimental data of rough external turning. This work is expected to assist computer numerical control machine tool practitioners in selecting optimal cutting parameters for minimum energy consumption. [ABSTRACT FROM AUTHOR]

Environmental Sustainability is one of the ‘Grand Challenges’ for manufacturing success in the 21st century. The present article mainly focuses on energy use during the manufacturing steps used for assembling products. In particular, industry still needs systematic and reliable ways to capture information for ultimately accounting for energy efficiency in manufacturing processes. For that purpose, an information model in the form of a formal ontology was created using information elements as building blocks, which emerge from manufacturing process models, supply chain operations, manufacturing process parameters, 3D form features and exergy-based thermodynamic analysis. These information elements were integrated by means of semantic relationships in an ontology. Once this information model was constructed, an application example of a welded hull panel assembly was used for verification of the model's representativeness and completeness. Results show that it is feasible to correlate all required data by means of semantic relationships for estimating energy efficiency indicators prior to physical prototyping, or ultimately comparing different alternatives for manufacturing processes regarding energy efficiency. However, manufacturing processes must be described in detail in order to allow energy efficiency analyses. In addition, geometric models need to be able to represent form features that can best relate to specific manufacturing process parameters. And finally, energy efficiency indicators, such as the degree-of-perfection can be derived from a comprehensive thermodynamic analysis, to be performed in each individual process step. Results indicate it is possible to provide an information structure for the calculation of the degree-of- perfection associated with an exergy-based thermodynamic analysis in assembly processes. Therefore, it has the potential to serve as a basis for a unifying framework for developing software applications that could allow the calculation of the degree of perfection in manufacturing operations. In addition, the resulting information model may facilitate data integration in the extended enterprise environment as well as the development of standards that can lead to seamless interoperability of manufacturing information systems. This paper presents the means by which energy efficiency can be accessed directly from an integrated semantic model, and opens the possibility to be extended for many different sorts of manufacturing processes. [ABSTRACT FROM AUTHOR]

Simulation of power flow in machine tools is crucial for future development of energy efficient machines as well as for energy optimization of existing machines, production systems and manufacturing processes. This paper describes analytical approach for modelling of energy consumption of auxiliary units of the CNC machine with corresponding activity management, because these units are usually not included in current models. The developed model is then compared with the measurement of the real 3-axis machine to prove the accuracy of the model. Results show the need to calibrate the real consumption of each unit instead of theoretical value from the plate of the unit. Without this calibration, models can be inaccurate. [ABSTRACT FROM AUTHOR]

In the highly competitive modern-day industrial landscape, characterized by globalization and resource scarcity, manufacturers are striving to improve economic and environmental performance. Innovation that enables self-adjustment, control and optimization of the energy consumption of individual machines continues. However, more research is needed if such systems are to be deployed successfully, especially considering the complex characteristics of the energy flows in the factory. In this paper we propose a novel approach to the coordination of information, processing and sensing systems for energy and resource efficient production systems. By leveraging on a recently-developed framework focusing on physical flows of energy, materials and waste we propose a solution based on specific energy efficiency KPIs and an online data acquisition/processing system, that enables real-time monitoring of the current status of the machining process and lagging assessment of system energy efficiency. The proposed solution allows the identification of abnormal energy consumption during the operational machine cycle, caused by incorrect part dimensioning or erroneous cutting conditions programmed by the process engineer, enabling identification of potential disruptions with different gravity levels, and delivery of meaningful alarms for the operator. Adaptive control of the machine cutting conditions or even trajectory re-programming is then possible, by correlating the energy-consumption data with other data, such as head temperature. Furthermore, by analysing the energy consumption of value and non value adding activities over complete production cycles (such as a shift or day), it is possible to monitor the progress of production systems toward achieving energy efficiency targets and to conduct root-cause analysis of inefficient energy usage for continuous improvement programs. We tested the proposed solution, modeling, index system ad online data acquisition/processing platform, through an industrial case study by deploying the developed hardware and software modules on a Nicolás Correa S.A. VERSA milling machine. [ABSTRACT FROM AUTHOR]

Energy management has been considered in the global agenda as a way to improve energy performance and greenhouse gas reduction in organizations. Industries account for a significant part of energy use worldwide and present opportunities for energy efficiency improvements. Within the industry, energy management is a complex task, regarding scenarios with variables related to the following perspectives: economics, contingency, technological change and behavioural. This paper aims at analyzing the influencing factors on energy management in industries from these perspectives. A survey with 40 variables was carried out with middle managers from different industrial sectors in Brazil. The variables were divided into three groups: drivers for investments in energy efficiency; organizational processes and actions in energy management; involvement of middle managers. Initially, an exploratory factor analysis technique was employed aiming at specifying the main factors influencing energy management. In the sequence , a confirmatory factor analysis was used to associate the variables to the main factors as well as to know how the factors relate to each other. The study showed a positive correlation among all the factors identified. Statistical tests suggested that the factors could not be explained separately. Hypotheses tests were applied to verify the influence of the factors among the groups surveyed. The final model comprised eight factors into the three groups: organizational (strategic , operational), involvement (motivation, support), drivers (production, economics , competitiveness, environment). The results and the main implications of the study are discussed in the paper. • The main factors on energy management presented a positive correlation among them. • Middle managers' involvement influenced strategy and operation on energy management. • Middle managers' involvement depended on the support and motivation from superiors. • Operational factor on energy management influenced drivers for energy efficiency. • Production process was a driver for energy efficiency influencing energy management. [ABSTRACT FROM AUTHOR]

Industrial manufacturing is the largest end-use sector in terms of both final energy demand and greenhouse gas emissions (more than 30% of the total); its increase is rapidly altering the world climate. The need to mitigate the environmental impacts of manufacturing processes makes energy efficiency a key success factor for sustainable production. Accordingly, the scientific community's interest in energy management has grown considerably, resulting in several literature reviews on energy modelling and production systems analysis, emissions calculation, sustainability tools and benchmarking techniques. However, a comprehensive analysis of methods and tools aimed at improving energy awareness and assessing their effects on energy efficiency is lacking. To address this gap, this paper undertakes a systematic literature review of energy assessment methods and tools. From the 1367 papers retrieved by searching scientific literature databases, 64 scientific articles met the inclusion criteria and were analysed in detail. The study aims to provide scholars with a picture of the current state of scientific research and to identify the scientific works that could help industry practitioners in energy management. Following the ISO 50001 framework, the methods and tools were divided into three main groups (i.e. energy analysis, energy evaluation and energy-saving measures methods) and the specific findings relating to each group were synthesized. Finally, the paper addresses unresolved issues and challenges and makes suggestions for future research directions. • A systematic literature review of energy assessment methods and tools are provided. • Relevant concepts, themes and characteristics has been summarized. • A classification of energy assessment methods and tools has been presented. • The limits and the possible solution strategies to overcome them are discussed. [ABSTRACT FROM AUTHOR]

In this paper, the optimization of the methanol production process is investigated. For this purpose, the parameters affecting methanol production including catalyst type (4 cases), temperature, pressure, and GHSV have been investigated and then by selecting the appropriate catalyst and process conditions, possible process changes such as hydrogen injection as make up, applying two reactors and inert gases, the use of dry hydrogen, and adding recycle stream on methanol yield have been studied. Then, by selecting the appropriate process, simulating, and validating the simulation results, the selected catalyst is used for the process. The process is simulated in 8 cases with changes in the studied parameters and the amount of recycle flow. Then, the key parameters of the optimized process were compared with the baseline. The results showed that, the investment costs (smaller dimensions of process equipment such as compressors, reactors, and distillation columns, etc. due to recycling flow reduction) and current costs (including electricity and steam consumption), significantly improved. In general, the use of CuZnOAl 2 O 3 catalyst in the methanol production process reduced the reactor temperature, decreased the recycle flow by about 38% and reduced the electrical energy consumption and steam consumption relative to the baseline by about 5% and 67%, respectively. Finally by the process optimization conducted in this work, in addition to reducing the recycle flow and reducing energy consumption, it is possible to annually reduce the GHG emissions of 7526.35 ton CO 2eq and 19.43 tons of air pollutants (per 100 kton/y of methanol production). • The process parameters affecting methanol production were optimized. • The recycle flow and energy consumption were reduced by 38% and 17%, respectively. • The GHG and air pollutants were decreased by about 16.9% relative to the baseline. [ABSTRACT FROM AUTHOR]

This paper presents the results of the first energetic and carbon footprint analysis in the manufacturing process of bamboo boards in Colombia. The energetic analysis was performed using energetic management tools, with the objective of estimating the energy consumption and the Consumption Index that characterizes the process. Life Cycle Assessment was used for carbon footprint analysis, taking into account the impact category of global warming following IPCC 2007, 100 years and having defined one bamboo board as the functional unit. The energetic analysis indicates an electricity value of 3456.6 kWh non-associated to the boards manufacturing, and a Consumption Index that recommends a minimum monthly production of 400 boards. The environmental analysis considered the avoided emissions due to prevention of remaining waste decomposition on the farming fields. The sum of generated and avoided emissions yields a negative value of – 117 kg of CO 2-eq . This result is due to the use of bamboo waste as a fuel for the boilers. We evaluated an alternative scenario of using natural gas as fuel. This alternative scenario also showed a high sensibility to the variation of critical supplies such as the fuel for the boilers. Which validates the environmental model. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic framework using Bayesian networks to aggregate the uncertainty from multiple sources for the purpose of uncertainty quantification (UQ) in the prediction of performance of a manufacturing process. Energy consumption, one of the key metrics of manufacturing process sustainability performance, is used to illustrate the proposed methodology. The prediction of energy consumption is not straightforward due to the presence of uncertainty in many process variables and the models used for prediction. The uncertainty is both aleatory (statistical) and epistemic (lack of knowledge); both sources of uncertainty are considered in the proposed UQ methodology. The uncertainty sources occur at different stages of the manufacturing process and do not combine in a straightforward manner, thus a Bayesian network approach is found to be advantageous in uncertainty aggregation. A dimension reduction approach through variance-based global sensitivity analysis is proposed to reduce the number of variables in the system and facilitate scalability in high-dimensional problems. The proposed methodologies for uncertainty quantification and dimension reduction are demonstrated using two examples – an injection molding process and a welding process. [ABSTRACT FROM AUTHOR]

Resource, economic, and environmental impacts are three major factors in the evaluation of industrial processes. All of them might imply certain contributions of ecosystems services. A comprehensive accounting of these factors is critical to sustainability assessment of industrial activities at the ecological scale. Ecological Cumulative Exergy Consumption (ECEC) analysis is a promising approach to address these issues. However, the current knowledge on ECEC is incomplete, especially in economic investment and environmental impact. In this paper, ECEC analysis is extended to quantify purchased resources and pollutant emissions of industrial production. Accordingly, an extended ECEC framework is proposed, integrating the resource, economic, and environmental factors. Furthermore, for better understanding of sustainability, a concept of Ecological Life Cycle Cost (ELCC) is put forward, revealing the relationship between the ECEC and traditional economic evaluation. Finally, a case study of China's raw coal production is used to illustrate the features of these proposed frameworks. The ECEC and ELCC analyses indicate the great ecological influence of raw coal which has been underestimated by the traditional assessment approaches. [ABSTRACT FROM AUTHOR]

Manufacturing industries are faced with environmental challenges, so their industrial processes must be optimized in terms of both profitability and sustainability. Since most of these processes are dynamic, the previously obtained solutions cannot be valid after disruptions. This paper focuses on recovery in dynamic job-shop scheduling problems where machines can work at different rates. Machine speed scaling is an alternative framework to the on/off control framework for production scheduling. Thus, given a disruption, the main goal is to recover the original solution by rescheduling the minimum number of tasks. To this end, a new match-up technique is developed to determine the rescheduling zone and a feasible reschedule. Then, a memetic algorithm is proposed for finding a schedule that minimizes the energy consumption within the rescheduling zone but that also maintains the makespan constraint. An extensive study is carried out to analyze the behavior of our algorithms to recover the original solution and minimize the energy reduction in different benchmarks, which are taken from the OR-Library. The energy consumption and processing time of the tasks involved in the rescheduling zone will play an important role in determining the best match-up point and the optimized rescheduling. Upon a disruption, different rescheduling solutions can be obtained, all of which comply with the requirements but that have different values of energy consumption. The results proposed in this paper may be useful for application in real industries for energy-efficient production rescheduling. [ABSTRACT FROM AUTHOR]

The stamping industry is critical in the automotive and aerospace industries, and the environmental impacts and sustainability of stamping systems have attracted considerable attention. To analyze the environmental impact and sustainability of stamping systems quantitatively, this paper presents an emergy-based evaluation method considering all the associated environmental impact factors of a pressing workshop including material and energy consumption, waste flows, time and mass losses, and processing costs. On the basis of analyzing the stamping processes and sustainability, this method comprises data collection, emergy based sustainability evaluation model and the development of sustainable evaluation indexes and systems. Based on the proposed method, the environmental impact of an industrial vehicle stamping workshop was evaluated. The results show that the net emergy yield ratio of the stamping system was greater than 2, and the waste emergy output ratio was very low, indicating that the system had high productivity levels, high yields, and low pollution discharge rates. However, its sustainability index was less than one owing to its high energy and material consumption during the production processes. To reduce the environmental impact of this stamping system and improve its sustainability, we investigated the potential improvements that could be achieved in energy consumption, productivity, and environmental impact by implementing energy-saving scheduling in the stamping workshop. The results show that the corresponding net emergy yield ratio and sustainability index of the system were both improved. The proposed emergy-based method could be used to quantitatively evaluate the environmental impacts and sustainability of stamping systems and facilitate efforts to reduce the environmental impacts of pressing workshops while improving their productivity and economic profitability. [ABSTRACT FROM AUTHOR]

Additive-subtractive hybrid manufacturing (ASHM) holds great potential for producing lightweight and ready-to-use parts with high accuracy within a single workstation. It is viewed as a promising technology to improve material efficiency and shorten manufacturing cycle. However, the energy-efficient manufacturing is not always achieved due to the huge electrical energy consumption in the manufacturing phase. It is not a simple "yes-or-no" question that ASHM process is more energy-saving than conventional subtractive manufacturing (CSM) process for production of a given part when considering the life cycle energy demand. For addressing this problem, this paper developed a decision-making model for quantitatively analyzing and comparing the energy demand of ASHM process and CSM process. The model takes into account the whole life cycle of the part, including pre-manufacturing phase, manufacturing phase, use phase, and end-of-life recycling. The critical variables of solid-to-envelope ratio, lightweight ratio, energy consumption reduction coefficient (ERC) are embedded in the model, and their synergistic effects on energy demand comparison are comprehensively analyzed. Moreover, the electrical specific energy consumption of the manufacturing phase was extrapolated as a function of the process parameters in the two manufacturing processes, and the unique relationship between specific energy consumption and process rate, laser power of the ASHM process is established, which can improve the generality of the decision-making model. The results indicated that parts used in aerospace are more suitable for production with ASHM process due to the lightweight and extremely high ERC, which leads to less energy consumption in the use phase compared to conventional manufactured parts. In static non-moving usage scenario, when the material recycling process is not considered, the manufacturing phase in ASHM process accounts for a large amount of total energy consumption, reached about 84%, while the material production in CSM process accounts for the largest proportion, reached almost 86%. In addition, process rate in ASHM process has an important effect on the electrical energy consumption in the manufacturing phase. By increasing the process rate from 1.5 to 5.5 g/min, the energy consumption of the manufacturing phase in ASHM process was saved by 38%. This work provides a quantitative method for decision-makers to select the sustainable manufacturing process when producing a functional part. • A decision-making model for analyzing and comparing the life cycle energy demand of ASHM and CSM was developed. • The critical variables of solid-to-envelope ratio, lightweight ratio, ERC are involved in the model. • The energy consumption of aerospace parts in use phase plays an important role in total energy consumption due to high ERC. • The energy consumption of the manufacturing phase accounts for approximately 84% of total energy consumption in ASHM process. [ABSTRACT FROM AUTHOR]

Hot-dip galvanisation (HDG) is the method most commonly used to protect steel surfaces from corrosion. However, HDG involves very intensive consumption of energy and resources. This paper aims to evaluate the environmental performance and hotspots in the Spanish HDG sector using cradle-to-gate life cycle assessment (LCA). Two Spanish HDG industrial plants, with different galvanisation capacities and manufacturing processes, were selected for the case study. The LCA revealed that the consumption of energy, fuels and nonrenewable resources were the most relevant environmental burdens at both plants. Steel was the main contributor, as it had the greatest influence on the plants' environmental profiles. The consumption of primary zinc and natural gas, used to dry and heat the molten zinc bath, also contributed to the impact of the HDG plants. This work proposes a normalisation to compare the Spanish sector against a European reference based on the average of 66 companies in 14 countries. The impacts of the Spanish HDG plants being studied were generally below the EGGA values, although the results are strongly influenced by the type of steel and the degree of material reuse implemented in the steel manufacturing process. The study lays the foundations for improvements in the resource efficiency and productivity of galvanisation plants. We propose alternatives such as the use of secondary zinc or modifications that will extend the lifespan of pickling baths, which would contribute to a more sustainable use of resources in the galvanising industry. Image 1 • The hot-dip galvanisation (HDG) sector in Spain was assessed via LCA. • Two HDG plants with different production properties provided detailed data inventories. • The highest impacts were found in the production of steel and primary zinc. • Using secondary zinc and extending pickling bath's lifespan would improve HDG. • The impacts of the Spanish HDG plants were generally below the European average. [ABSTRACT FROM AUTHOR]

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

Numerous process integration techniques were proved to be highly effective for identifying and estimating potential energy savings in the industry. However, they require high time and effort to collect and analyse process data. As a result, they do not constitute the common practice in the industry and opportunities for increasing the energy efficiency of industrial processes are missed. The paper presents a method, termed the " Energy-Saving Decomposition ", which is based on Process Integration techniques. It is intended for expeditiously outlining and promoting energy efficiency in the industry. Two screening tools, based on mathematical criteria and engineering experience, are employed for reducing the problem dimension before applying conventional design procedures. The first step disregards streams based on their contribution to the overall energy-saving potential, calculated utilising a novel energy-saving decomposition technique. The most promising network is then selected based on its energy-saving potential and size. The second step reduces the problem complexity further, employing economic considerations. This novel method was exemplified by application to a dairy factory: the outcomes and the method itself were compared to conventional Pinch Analysis techniques. The results showed that the developed method can simplify and reduce the time consumption of conventional Process Integration methods significantly, while identifying the most encouraging saving opportunities. The automatic algorithm allowed for reducing the problem size from 62 process streams of the existing plant to 22 streams requiring a computational time of only 135 s. The final retrofit design proposed was the same obtained with conventional Pinch Analysis, achieving a 23% reduction in the plant final energy consumption. • A novel expeditious method for process integration retrofit studies is presented. • A novel decomposition of the energy-saving potential allows a reduction in problem size. • A case study proves that the problem size is reduced from 62 to 22 streams with no loss in favourable design options. • The method can be automated and is fast to compute, requiring a few minutes. [ABSTRACT FROM AUTHOR]

The climate mitigation and the reduction of energy cost in manufacturing processes drive to expand the electricity generation from renewable sources. Nonetheless, intermittency of renewable energies, especially solar and wind energy, represents one on the main challenge, typically overcome by the installation of electricity storage systems. This issue can be addressed by a new and original approach, consisting in energy-flexibility of the production, in which manufacturing parameters are selected to optimize and to align production planning to the renewable energy availability. The paper deals with a time dependent theoretical and numerical model developed to calculate the time evolution of the electric power required by a manufacturing system, self-consistently coupled with a renewable plant. The aim of the model is to align the power required by the manufacturing system with the renewable energy supply in order to obtain the maximum monthly profit. The model has been applied to a single work center powered by the electric grid and by a photo-voltaic system, performing the machining process over one year of production. The model includes the tool cost, the stocked units, the energy cost and the penalty for the unsatisfied demand. The maximum profit has been calculated with a hourly adaption of manufacturing parameters, i.e. the cutting speed, to the renewable time dependent power profile. The model presents general features and can be applied when production processes are fully characterized. In order to find the maximum profit, the model, inherently nonlinear, has been solved by recurring to the Trust-Region Method. Different scenarios characterized by fluctuations of product demand are considered in order to investigate the sensitivity of the manufacturing system to the uncertainty of the forecast demand. The influence of the photo-voltaic supply has been investigated, comparing results obtained in the case of manufacturing systems powered only by the electric grid. Numerical results show how the proposed method allows to select an optimized production planning, reducing the energy costs and CO 2 emissions and finding the maximum profit with the best compromise between the market demand and energy costs. • Energy-flexibility consists in the alignment of production to renewable sources. • The optimization method maximizes the profit with a photovoltaic plant. • Time dependent machine parameters are controlled by coupling with renewable source. • The optimization method can work with the uncertainty of the forecast demand. • The optimization method reduces the energy bought from the grid and CO 2 emission. [ABSTRACT FROM AUTHOR]

With the increase in awareness of energy conservation and emission reduction in the manufacturing sector, the energy efficiency optimization of the production process is considered an effective way to promote cleaner production and environmentally sustainable development. Moreover, the transportation process and its energy consumption are non-negligible during the entire workshop production process. In practice, cutting tool degradation during operation is inevitable, which causes the actual processing time of work to be extended, so it is necessary to consider the degradation effects and imperfect maintenance. To reduce the total energy consumption in the manufacturing workshop with degradation effects and imperfect maintenance, this paper constructs a novel integrated optimization model including the flexible job-shop scheduling problem (FJSP), forklift transportation and imperfect cutting tool maintenance. First, an imperfect cutting tool maintenance with degradation effect strategy is presented, which makes the actual processing time of each operation more closely approximate the real-world processing situation. Second, an integrated multi-objective optimization model is formulated to simultaneously minimize the makespan, total tardiness, total production cost and total energy consumption. Third, a hybrid multi-objective evolutionary algorithm with Pareto elite storage strategy (HMOEA/P) is proposed to address the model. More precisely, three improved operations are presented: hybrid dominance principle, local search algorithms and Pareto elite preservation strategy. The effectiveness and feasibility of the parameter setting, improved operations and the proposed algorithm are separately demonstrated by the comparative experiments. Last, the superiority of the integrated multi-objective optimization model is proven by comparing different maintenance strategies and energy consumption models. [ABSTRACT FROM AUTHOR]

As mechanical engineering is growing a current and urgent issue is rising in the manufacturing process, which is the ability to improve efficiency while reducing energy consumption during the processes. Cutting parameters are an important part of the computer numerical control (CNC) machining process, so a reasonable selection of cutting parameters can significantly enhance the machine's energy efficiency. Previous studies mainly focused on cutting power (P) and material removal rate (MRR), respectively, without considering the trade-off relationship between them; In this paper, an optimization model of cutting parameters is developed by establishing a multi-objective model where P and MRR are identified. The proposed model utilizes the Grey Correlation Analysis (GRA) and experimental to determine the weight of the objective. After the Linear Decreasing Particle Swarm (LDPS) optimization algorithm was utilized to solve the model, several application cases are given and their results demonstrate the ability of our method through comparing with the traditional approach. [ABSTRACT FROM AUTHOR]

Nowadays the manufacturing enterprises are driven towards significant energy improvements by increasing energy costs and sustainability awareness, and energy consumption and energy efficiency are taking a more and more dominating position in the decision-making process of manufacturing enterprises. However, in the process of production, existing researches rarely integrate energy management with conducting preventive maintenance at the strategic level, ignoring energy-saving opportunities that may arise during machines' downtime. This paper considers a serial production system with intermediate finite buffers. Based on the concept of "energy-saving opportunity window" and the modelling of the continuous deterioration process of each machine, the window of energy-saving opportunity of the production system is regarded as a window of opportunity for the preventive maintenance, thus a proactive preventive maintenance strategy is proposed. The serial production system is modeled and the impact of different maintenance thresholds on the energy efficiency is explored by means of Monte Carlo simulation. For purpose of maximizing the energy efficiency of the entire production system, an iterative algorithm for solving the optimal preventive maintenance thresholds is presented as well. The numerical analysis with other maintenance strategies shows that the proposed strategy is more competitive in improving the effective throughput while reducing the overall energy consumption. • A proactive condition-based preventive maintenance policy was proposed. • Integrating the concept of energy-saving window (ESW) and the quality loss into an optimization production model. • An energy-related objective function, effective energy efficiency (EEE), was developed. [ABSTRACT FROM AUTHOR]

The manufacturing sector consumes a significant amount of energy and their outputs, like solid and gaseous waste streams, can result in substantial stress on the environment. This paper aims to analyze and compare the electrical energy and material efficiency of machining, additive and hybrid manufacturing. The analysis of the manufacturing processes is based on machine tool data from a sample process. To get a generalized statement about the energy consumption of the investigated processes the electrical energy demand was extrapolated as a function of the material removal ratio. The results indicate that hybrid manufacturing becomes beneficial from an environmental point of view compared to milling, when the material removal ratio exceeds 55%. The electrical break-even point for selective laser melting is approximated to 82% material removal ratio from data extrapolation. Subsequently, opportunities for electrical energy and material efficiency improvements are presented for these technologies to gain an understanding of how each can contribute to a more sustainable manufacturing landscape. • Additive processes show a smaller amount of wasted material. • For material removal ratios over 55% additive processes show less demand of energy. • For material removal ratios over 75% additive processes show less processing time. [ABSTRACT FROM AUTHOR]

This paper investigates an order acceptance and scheduling problem with an energy consumption budget, a machine launch budget, and the order release time in a green manufacturing system. The phenomenon of deteriorating jobs is considered in the production of the accepted orders. The objective of the study is to maximize the net revenue and a modified variable neighborhood search (MVNS) Algorithm that combines a novel encoding and decoding procedure as well as a dynamic programming algorithm is developed to solve it. To show the effectiveness and efficiency of the proposed algorithm, we first employ the MVNS algorithm and seven VNS-based algorithms to solve the problems with different configurations of orders and machines. The results show that the MVNS algorithm obtains better solutions than existing VNS-based algorithms. Then, the proposed algorithm is compared with three meta-heuristic algorithms. The experimental results show that the proposed algorithm has significant advantages in terms of solution optimality. [ABSTRACT FROM AUTHOR]

Manufacturing industry accounts for about one-third of the world's total energy consumption (TEC). This study aims to develop a novel mixed-integer mathematical model to represent the direct energy consumption of machines and indirect energy consumption on a shop floor. In comparison with traditional modeling methods, this paper proposes an effective gene expression programming-based rule mining (GEP-RM) algorithm to generate dispatching rules automatically. This method consists of three attributes that have significant impacts on the TEC of a manufacturing process. In addition, diversified rule mining operators with self-learning are designed to ensure population diversity and convergence. Moreover, a perturbation trigger mechanism for reconstructing rules is introduced to avoid being trapped into a local optimum. An unsupervised learning algorithm is achieved by setting the evolution direction with global best and current worst in order to mine the value of the substantial historical data. Experimental results have shown that the proposed multi-attribute rule mining approach outperforms other dispatching rules in terms of energy saving and production efficiency. [ABSTRACT FROM AUTHOR]

This study deals with the interface between energy consumption and its related emissions in the manufacturing of one cubic meter medium density fiberboard (MDF) in Iran and provides a comprehensive life cycle inventory (LCI) data for production process. The study was developed following the methodological guidelines of ISO 14040. Inventories data for a gate-to-gate assessment of MDF manufacturing were collected by on-site surveys. Ten impact categories including of: Abiotic depletion (AD), Acidification (AC), Eutrophication (EP), Global warming (GW), Ozone layer depletion (OLD), Fresh water aquatic ecotoxicity (FE), Marine aquatic ecotoxicity (ME), Human toxicity (HT), Terrestrial ecotoxicity (TE) and Photochemical oxidation (PO) in three subsystem of manufacturing process were assessed. The subsystem of fiber preparation was the major contributor to the environmental impacts. Urea-formaldehyde (UF) resin production was identified as the main hotspot, except in AD, AC, OLD and PO impacts that natural gas, electricity, transport and production process were the main hotspots, respectively. Moreover, Tube dryer, MDF saw and hogger were the most effective sectors in the emissions of production process. Results for substitution of natural gas showed that except in some impacts categories, use of alternative source of thermal energy such as wood residue could improve emissions from manufacturing. [ABSTRACT FROM AUTHOR]