Search

Drivers for industrial energy efficiency are factors that promote the sustained adoption of energy-efficient measures and practices. Leveraging drivers to overcome barriers and encourage action which improves industrial energy efficiency can contribute to closing the energy efficiency gap. In fossil-fuel-based systems, this will also contribute to greenhouse gas abatement. A systematic literature review was used to investigate how knowledge about drivers is generated and whether prevalent drivers can be mapped to existing taxonomies. The systematic literature review confirmed that surveys and/or interviews with managers from countries who are members of the Organisation for Economic Cooperation and Development (OECD) are the most common way to gather data on drivers for industrial energy efficiency. This means the extant knowledge on drivers may be incomplete because contributions from some stakeholders, industry types and company sizes may be missing. The review also found economic drivers are the most prevalent and that not all the drivers identified during the study can be mapped to contemporary driver taxonomies. Having an agreed-upon comprehensive taxonomy facilitates empirical research and comparison of studies. Further research into the views of frontline workers and the creation of a comprehensive driver taxonomy is recommended. [ABSTRACT FROM AUTHOR]

Rising energy costs and the current concern about sustainability issues directly impact the competitiveness and survival of organizations, promoting the search for continuous improvement in industrial production processes. The present study, by identifying the absence in the literature of models based on Value Stream Mapping and integrated with Industry 4.0 technologies that promote better energy efficiency practices in industrial processes, proposes a new Lean Management model called Real-time Energy Flow Mapping. This innovative model, provide capabilities to a visual, digital and sustainable management system of all energy behavior in machines and equipment using fully parameterizable metrics and rules. Also, estimulating stakeholders with the ability to predict and monitor energy costs, as well as identify possible problems due to changes in the behavior of energy consumption in machines, thus helping managers to make decisions faster and more dynamically. As a result, the literature search turned up 17 Value Stream Mapping based models with a focus on energy efficiency, along with 31 metrics grouped on energy, environmental and production processes. After discussions by the Focus Groups, 16 resulting metrics were submitted to expert panel, in which the least relevant were discarded and the 13 most relevant classified using Fuzzy Delphi. An analysis to verify the interrelationships between the metrics is performed using the Fuzzy DEMATEL, being finally applied in the proposed model. A case study of the model was carried out, submitting it operationally at the LabFaber 4.0, empirically validating the study. The Real Time Energy Flow Map fills the gap in the literature on Value Stream Mapping models to improve energy efficiency, monitoring energy consumption through dynamic mapping of the energy flow of production processes, identifying the area with increased potential for energy savings through changes in operating behavior that reduce overall energy consumption and environmental impacts. • Lean model integrated with energy, environmental and production process metrics. • Value Stream Mapping model for best practices in energy efficiency. • Real-time energy consumption modeling of industrial production processes. • Lean model in a real-world case study to analyse the benefits of energy efficiency. • Analysis of the most relevant energy, environmental and production process metrics. [ABSTRACT FROM AUTHOR]

• Energy audit and management of an industrial site (SME) in Egypt are investigated. • Installating VSDs would save 48.477 MWh and mitigate 21 t of CO 2 annually. • Thermal insulation of the mold assembly saves nearly 319 MWh per year. • PV system expansion would satisfy about 50.95 % of the factory's annual consumption. • Energy savings of 535.97 MWh and 232 tons of CO 2 mitigations are expected annually. This study presents an energy audit and management for an industrial site in Borg El-Arab City, Egypt. Notably, the investigated industrial site falls in the category of small and medium-sized enterprises (SMEs), which are characterized by a high share of final energy use and possess a significant untapped energy-efficiency potential. In this work, potential sources of energy wastage in the facility have been investigated following a unique energy audit and analysis road map. Furthermore, tailored energy efficiency recommendations and effective energy management practices have been proffered for the various industrial equipment available for the first time. Besides, the proposed action plans are economically evaluated using the payback method and their environmental benefits are assessed accordingly. The results show that a proposed expansion of the PV system would produce 676.62 MWh/year and satisfy about 50.95 % of the factory's annual electricity consumption. The renewable energy system presents an investment cost of $124,115 with an attractive payback period of about 3 years alongside curbing 293 tons of CO 2 emissions annually. In addition, the installation of variable speed drives in the motor systems would save about 48.477 MWh/year with a payback period of 1.73 years and mitigate about 21 t of CO 2 annually. Nevertheless, thermal insulation of the mold assembly using thermal boards and incorporating an air gap in the mold assembly design results in energy savings of about 319 MWh per year. The energy management of fans, compressors, and lights demonstrates annual energy savings of 121.461 MWh, 14.88 MWh, and 32.154 MWh, respectively. Notwithstanding, replacing fossil fuel-based forklifts with electric forklifts leads to an investment cost of $32,220 with an associated payback period of 5.74 years. Noteworthy, the energy audit and management herein present energy savings and CO 2 emissions mitigations of 535.971 MWh and 232 t annually, respectively. Evidently, the energy audit and management performed will act as a guide for relevant decision-makers towards energy efficiency improvements, consequently achieving economic savings and limiting the carbon footprint. [ABSTRACT FROM AUTHOR]

Implementing energy efficiency measures is widely regarded as a high priority in the search for carbon dioxide emission reductions and can create competitive advantage for industry. Yet, approximately 50% of industry's cost effective energy efficiency measures are never implemented. The lack of implementation of energy efficiency measures results in the energy efficiency gap, which is generally attributed to barriers. Attempts to close the gap are directed at overcoming barriers and leveraging drivers. However, the current view of barriers and drivers may be incomplete due to the under representation of frontline workers' perspectives in research. Frontline workers strongly influence energy use because they make decisions about how and when industrial equipment is operated and maintained. This study compares survey responses from frontline workers and managers to investigate the barriers and drivers to industrial energy efficiency. Analysis of free text responses found perceived barriers and drivers for energy efficiency are different for frontline workers and managers. Differences between frontline workers' and managers' views are less prominent when only responses to Likert-type questions are considered. In response to this, new taxonomies for barriers and drivers were created. It is proposed these new taxonomies will be useful for empirical research as they can incorporate more complete views of frontline workers and managers. Common methods used to overcome barriers may have limited impact on the barriers found in this study. This more complete understanding of barriers and drivers could be useful for industry to characterise their challenges, contribute meaningfully to policy creation and generate new ways to close the energy efficiency gap. The novelty of this research stems from its investigation and presentation of the frontline workers' perspective. Further research into drivers, barriers and data gathering for frontline workers is needed to validate this exploratory study. • Frontline worker perspectives on barrier and drivers are presented. • Barriers and drivers for frontline workers and managers show some differences. • Common methods to overcome barriers may have limited impact on some of the barriers identified. [ABSTRACT FROM AUTHOR]

There has been increasing interest in reducing carbon footprints globally in recent years. Hence increasing share of green energy and energy efficiency are promoted by governments. Therefore, optimizing energy consumption is becoming more critical for people, companies, industries, and the environment. Predicting energy consumption more precisely means that future energy management planning can be more effective. To date, most research papers have focused on predicting residential building energy consumption; however, a large portion of the energy is consumed by industrial machines. Prediction of energy consumption of large industrial machines in real time is challenging due to concept drift, in which prediction performance deteriorates over time. In this research, a novel data-driven method multi-regime approach (MRA) was developed to better predict the energy consumption for industrial machines. Whereas most papers have focused on finding an excellent prediction model that contradicts the no-free-lunch theorem, this study concentrated on adding potential concept drift points into the prediction process. A real-world dataset was collected from a semi-autonomous grinding (SAG) mill used as a data source, and a deep neural network was utilized as a prediction model for the MRA method. The results proved that the MRA method enables the detection of multi-regimes over time and provides a highly accurate prediction performance, thanks to the dynamic model approach. [ABSTRACT FROM AUTHOR]

Industry is an essential part of the search for greenhouse gas emission reductions. Implementing energy efficiency measures is one way industry can optimise energy use and reduce greenhouse gas emissions. Previous research has highlighted that a number of barriers impede the implementation of energy efficiency measures, creating the energy efficiency gap. This study explores the barriers that impede implementation of energy efficiency measures by combining the results from a systematic literature review and primary research. The systematic literature review found information on barriers is predominantly generated using surveys and interviews with company managers and company size may have more influence on barriers than energy intensity. The primary research showed barriers reported by frontline workers are different to those experienced by managers and that drivers contribute action on energy efficiency. The results also showed existing taxonomies are not well suited to representing frontline workers' views. Thus, attempting to close the energy efficiency gap by focusing solely on barriers, may create a new barrier, particularly if the roles of frontline workers and drivers continue to be overlooked. Further research is proposed to develop and empirically test extensions to the current driver and barrier taxonomies so the views of all stakeholders can be meaningfully incorporated. • Frontline workers are not well represented in literature on barriers to energy efficiency. • The importance of drivers in improving energy performance may be underestimated. • Barrier taxonomies need further development to accurately capture energy efficiency challenges and opportunities. [ABSTRACT FROM AUTHOR]