Industrial Energy Management Research Articles

A Novel Hybrid-Action-Based Deep Reinforcement Learning for Industrial Energy Management

A Novel Hybrid-Action-Based Deep Reinforcement Learning for Industrial Energy Management

A novel approach to energy management in the dairy industry using performance indicators and load profiles: Application to a cheese dairy plant in Tuscany, Italy

The available energy monitoring information in the dairy industry reveals significant gaps regarding the impact of operational factors on performance indicators and the generation of typical days from load profiles. This paper presents a method for predicting performance indicators and load profiles in the dairy industry based on multiple regression and clustering. The method is applied to a cheese dairy plant located in Tuscany, Italy, providing actionable insights for energy efficiency and renewable integration. With regard to performance indicators, predictions using multiple regression achieved accuracies within 8 % for electricity consumption and within 20 % for steam generation, mainly due to limited data availability. The combination of k-means clustering with multiple regression yielded an overall accuracy within approximately 10 % for electricity load profiles, enabling the labelling of clusters based on production and meteorological variables. The analysis of improved production planning and the compatibility of energy demand with solar resources showed potential reductions in the electrical performance indicator by 27 % and self-consumption rates between 14 % and 42 %, respectively. Validation with data from other dairy contexts confirms the accuracy of the method and the considerable potential for improvement, suggesting further implementation towards effective energy management in the dairy industry.

Technological Innovation in Energy Management: A Case Study of Implementation in the Industrial Sector

This study explores technological innovations in energy management within the industrial sector through a detailed case study implementation. The research aims to analyze the impact of new technologies on enhancing energy efficiency and sustainability. Methodologically, a comprehensive case study approach is employed to investigate the practical application of these innovations in an industrial setting. The findings highlight significant improvements in energy consumption monitoring and operational efficiency, facilitated by advanced technological solutions. This research contributes valuable insights into the transformative potential of technology in industrial energy management.

Study Of Energy Management And Efficiency In Industrial Processes: Analysis Of Implementation And Its Impact On Productivity

This study focuses on the implementation and analysis of energy management and efficiency in the context of industrial processes to enhance productivity. Drawing upon literature review on the fundamental concepts of energy management and best practices in industries, this research identifies strategies and methods that can be applied to optimize energy use in manufacturing environments. The research methodology involves gathering data from various sources to evaluate the practical implementation of energy management strategies in manufacturing industries, including pre- and post implementation data analysis. The findings provide deep insights into the effectiveness of specific measures in reducing energy consumption, improving operational efficiency, and their overall impact on productivity. The study also highlights challenges that may arise in implementing energy management strategies, such as initial costs, staff training needs, and technology integration. However, the long-term benefits of efficient energy management include reduced operational costs and environmental sustainability. By combining technical and managerial approaches, this research provides a foundation for better decision-making in adopting sustainable practices in the industrial sector.

Development and experimental assessment of a multi-annual energy monitoring tool to support energy intelligence and management in telecommunication industry

Carbon-footprint reduction of key industrial buildings is addressed, by proposing methodologies for continuously monitoring telecommunication (TLC) central offices (COs). Main aim is classifying sites according to their efficiency and reliability, via the diagnosis of anomalous electricity consumptions. Such a goal is achieved through the definition of new key performance indicators (KPIs) based on TLC and cooling energy demand, improving the outcomes of pre-existing methods. While the reliability index and index of cluster reliability are specifically proposed to evaluate and physically assess the impact of climate control (CLC, i.e., the parasitic quota) electricity consumption with respect to the TLC one, the here introduced coefficient of variation of telecommunication energy allows for a more solid evaluation of energy measurements reliability. Another target of this study is to extend the afore-mentioned KPIs-based analysis to multi-annual periods of monitoring, thus allowing successfully meeting the currently in-force ISO 50001 standard. Specific central offices were then selected and analyzed to verify the results physical meaning. The method was proven effective in classifying central offices belonging to climate-homogenous fleets, according to the reliability level estimated over a triannual timeframe. Positive impacts in terms of attainable energy saving through improved thermal management, as well as methodology extendibility to other industrial sectors are finally presented and discussed.

Predictive energy control for grid-connected industrial PV-battery systems using GEP-ANFIS

Rising energy costs in Uganda's industrial sector have significantly increased manufacturing expenses and led to the closure of potential enterprises, underscoring the urgent need for efficient energy management solutions. The study presents an optimization-based predictive energy control model that integrates Gene Expression Programming (GEP) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) for grid-connected solar PV-battery systems. The hybrid GEP-ANFIS model leverages GEP's strength in complex pattern recognition and ANFIS's adaptive learning capability to manage nonlinear data, resulting in more accurate and reliable energy predictions. The model utilizes historical load patterns, grid data, and solar PV/battery inputs to forecast load demand and solar power generation, optimizing energy usage based on Time of Use (TOU) pricing. The approach addresses the limitations of existing energy management techniques by enhancing predictive accuracy and ensuring seamless integration with industrial applications. The present work demonstrates that the proposed GEP-ANFIS double diode model consistently outperforms its counterparts, achieving a mean absolute percentage error (MAPE) of 7.25 %, a mean absolute deviation (MAD) of 2.95 %, and a root mean square error (RMSE) of 8.42 %. Moreover, the model exhibits superior accuracy in predicting short-term load demand, with a MAPE of 2.24 %, a MAD of 0.13 %, and an RMSE of 0.18 %. Additionally, the model results in an average energy cost reduction of 6.7 % for hybrid grid-connected solar PV/battery configurations, highlighting its economic benefits. The study's key contributions include the creation of a novel hybrid predictive model, significant cost savings, improved load prediction accuracy, and the integration of renewable energy sources into industrial energy management. The model's inferences can effectively address the challenges of rising energy costs, providing a robust framework for sustainable and efficient energy management in industrial settings.

Advancing Industrial Process Electrification and Heat Pump Integration with New Exergy Pinch Analysis Targeting Techniques

The process integration and electrification concept has significant potential to support the industrial transition to low- and net-zero-carbon process heating. This increasingly essential concept requires an expanded set of process analysis tools to fully comprehend the interplay of heat recovery and process electrification (e.g., heat pumping). In this paper, new Exergy Pinch Analysis tools and methods are proposed that can set lower bound work targets by acutely balancing process heat recovery and heat pumping. As part of the analysis, net energy and exergy load curves enable visualization of energy and exergy surpluses and deficits. As extensions to the grand composite curve in conventional Pinch Analysis, these curves enable examination of different pocket-cutting strategies, revealing their distinct impacts on heat, exergy, and work targets. Demonstrated via case studies on a spray dryer and an evaporator, the exergy analysis targets net shaft-work correctly. In the evaporator case study, the analysis points to the heat recovery pockets playing an essential role in reducing the work target by 25.7%. The findings offer substantial potential for improved industrial energy management, providing a robust framework for engineers to enhance industrial process and energy sustainability.

Performance modification of an acid gas incinerator to reduce atmospheric pollutants impact: Energy management, HAZOP and LCA analyses

In today's industrial landscape, energy management, process modification, and reduction of atmospheric concentrations of pollutants and safety risks have become paramount. This focus is driven by the need to address environmental concerns, economic efficiency, and the global energy and climate change crisis. In gas refineries, incinerators are widely used to convert deadly and environmentally polluting acid gases into less hazardous gases. Therefore, improving incinerator performance can significantly impact environmental, economic, and energy aspects. According to the results of an energy management study at the domestic gas processing plant, the acid gas incineration unit was identified as a significant energy use. Therefore, based on the effects of the performance of this incinerator from environmental and energy points of view, the mentioned unit was prioritized for modification in this work. For this purpose, incinerator performance was assessed using Promax simulation, and Hazard and Operability (HAZOP) analysis was employed to identify potential hazards. The simulations revealed that acid gas residence time was 0.81s, longer than the 0.6s initial design with the damper in place. This suggests damper removal is feasible. Removing the damper reduces residence time and lowers incinerator temperature, especially during startup. Therefore, temperature was considered as the keyword in the HAZOP study, and a number of recommendations were proposed to eliminate or mitigate the risks of system modification. Furthermore, the assistance of results obtained from energy management based on ISO 50001:2018 standards confirm improvements in energy efficiency and fuel consumption, which have positive economic and environmental impacts. Moreover, the study employs a Life Cycle Assessment (LCA) approach using SimaPro Software 9.5.0.1 and the CML-baseline method (Centrum voor Milieukunde Leiden) for environmental impact assessment. The results reveal that, across ten environmental impact categories, the modified project exhibits significantly reduced environmental impacts compared to its original state.

Machine Learning and image analysis towards improved energy management in Industry 4.0: a practical case study on quality control

With the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.

AI-Powered Energy Management System for Industry

The industrial sector, serving as a linchpin of global economic growth, continually shapes innovation, job creation, and wealth accumulation. However, its indispensable role is juxtaposed with substantial energy consumption, contributing significantly to the world’s energy usage and environmental footprint. In their pursuit of heightened competitiveness and profitability, industries confront a pressing challenge: the judicious management of energy resources. Striking a harmonious equilibrium between cost-efficiency, operational excellence, and environmental sustainability presents a formidable complexity. Compounding this complexity is the reluctance of traditional energy management methodologies to adapt to the dynamic and data-rich landscapes characterizing modern industrial domains. This research paper ventures into the heart of these challenges, exploring the convergence of the industrial sector, energy management, and technological advancements. Within this multifaceted landscape, it scrutinizes the intricacies of contemporary industrial environments, exposing the inherent complexities of responsible energy management. With data-driven technologies and advanced analytics as guiding lights, this study illuminates a transformative path forward—transcending conventional paradigms to forge a new era of efficiency, sustainability, and profitability

SMART GRIDS IN INDUSTRIAL PARADIGMS: A REVIEW OF PROGRESS, BENEFITS, AND MAINTENANCE IMPLICATIONS: ANALYZING THE ROLE OF SMART GRIDS IN PREDICTIVE MAINTENANCE AND THE INTEGRATION OF RENEWABLE ENERGY SOURCES, ALONG WITH THEIR OVERALL IMPACT ON THE INDUSTRI

This study provides a comprehensive analysis of the integration and impact of smart grids in the industrial sector, focusing on the evolution from traditional grids, the role of predictive maintenance, and the integration of renewable energy sources. The primary objectives were to understand how smart grids are reshaping industrial energy management and to assess their impact on efficiency, sustainability, and operational practices. A systematic literature review was employed as the methodology, utilizing electronic databases and academic journals to gather relevant literature from 2018 to 2023. The search strategy involved keywords related to smart grids, predictive maintenance, and renewable energy integration. Inclusion and exclusion criteria were established to ensure the relevance of the selected literature, which was then thematically synthesized to identify key trends and insights. Key findings indicate that smart grids mark a significant shift in energy management, offering enhanced efficiency, reliability, and sustainability. The integration of renewable energy sources addresses challenges like intermittency, contributing to environmental sustainability. Predictive maintenance within smart grids has emerged as a crucial strategy for improving operational efficiency and reducing maintenance costs. The study also highlights the economic and environmental impacts of smart grid technologies, underscoring their potential in promoting sustainable industrial practices. Finally, smart grids represent a pivotal advancement in industrial energy management. The study recommends continued innovation and strategic implementation to realize their full potential, with future research focusing on AI applications, emerging renewable sources, and policy frameworks in smart grid technologies.
 Keywords: Smart Grids, Predictive Maintenance, Renewable Energy, Industrial Efficiency.

Development and Evaluation of an Intelligent Control System for Sustainable and Efficient Energy Management

This paper presents a comprehensive study on the integration of Intelligent Control Systems in the global industrial sector, focusing on enhancing energy management through the synergy of Supervisory Control and Data Acquisition (SCADA), Machine Learning (ML), and Digital Twin technologies. We elaborate on a novel ICS architecture designed to optimize energy consumption, reduce operational costs, and minimize environmental impacts. Our system leverages SCADA for real-time monitoring and control, ML algorithms for predictive analytics and optimization, and Digital Twin technology for advanced simulation and operational efficiency. The implementation of the system in a mid-scale industrial facility demonstrated significant improvements: a 15% reduction in energy consumption, an 18% decrease in peak energy demand, a 30% reduction in CO2 emissions, and a 15% reduction in operational downtime, with predictive accuracy standing at 90%. These results underline the potential of integrating advanced digital technologies in industrial energy management, offering a scalable model for sustainable and efficient industrial practices. Future work will explore broader applications and the incorporation of emerging technologies to further enhance the system's capabilities and applicability in diverse industrial settings.

Industrial cluster energy systems integration and management tool

Industrial cluster energy systems integration and management tool

Regional deep energy Q-Net-based energy scheduling for industrial energy management system

Regional deep energy Q-Net-based energy scheduling for industrial energy management system

Human Energy Management in Industry: A Systematic Review of Organizational Strategies to Reinforce Workforce Energy

Recent global events, including the pandemic, social unrest, geopolitical instabilities, automation risk, and socio-economic upheavals, have amplified challenges and stressors that deplete human energy. Coupled with work-related stress, these factors substantially strain the invaluable human capital that fuels organizational productivity. Many scholars acknowledge the existence of a global human energy crisis. While industry may not be in a position to control these stressors, leaders and managers can positively influence employee experience by optimizing workforce energy. Via a systematic literature review adhering to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, this study aims to explore (1) the concept of human energy and its importance in a human-centered industry, (2) promoting a culture of human energy management within an organizational context, and (3) the existing approaches and strategies that manage human energy. The search in two databases, Scopus and Web of Science, found 1084 papers. After identification and screening, 86 documents remained, and 32 papers was retained after title, keyword, and abstract assessment. Subsequently, a full-text analysis yielded 18 papers included in the review. This study emphasizes industry’s potential to address a societal challenge, “the human energy crisis”, aligning with the European Union’s vision of a sustainable and resilient society.

A non-functional requirements-based ontology for supporting the development of industrial energy management systems

Non-functional requirements (NFRs) are essential in the development of industrial energy management systems (IEMS). This paper presents a comprehensive ontology model for NFRs in IEMS, derived from an extensive survey of NFRs in both the software industry and the energy domain. The proposed ontology encompasses four critical factors influencing the quality attributes of IEMS: technologies, stakeholders, markets, and regulations. Implemented using the OWL 2 DL standard, the ontology model aims to provide a clear and consistent understanding of NFRs and their relationship to the energy domain, as well as identify which factors have a significant impact on the environmental performance of IEMS. The ontology is evaluated through various methods, such as technical validation, user evaluations, its applications in RDF data management, and application-based evaluation, including software architecture, knowledge base data model design, and regulatory framework design. By understanding the link between software quality requirements and the characterizing factors of the energy domain, as provided by our ontology model, this information can be used to inform life cycle assessments and quantify potential reductions in energy consumption, emissions, waste generation, and other environmental impacts associated with implementing cleaner and more sustainable solutions. The results demonstrate that the proposed ontology effectively supports IEMS development and serves as a foundation for developing reusable and adaptable software systems across different industrial domains.

A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-making process considering different objectives, scheduling algorithms, and implementations. Thus, based on our insights, we provide numerous recommendations for future research in residential and industrial energy management systems.

Optimization of site utility systems for renewable energy integration

Considerable attention has been paid to the electrified energy supply based on renewable energy for achieving carbon-neutrality. A systematic and integrated approach is required to identify optimal operating strategies for the integration of electrified energy sources with conventional utility systems and to understand the techno-economic impact of using renewable energy on industrial energy management. Renewable-integrated industrial utility systems are modeled, which is optimized to investigate economic trade-off between capital investment, fuel consumption, power generation, and CO2 emission tax. Sensitivity of key design parameters is examined with the optimization framework, which allows to gain conceptual understanding on the economic impact of CO2 emission tax and prices of renewable electricity on site-wide heat and power management. Compared to the utility system based on the combustion of fossil fuels only, the operating cost of renewable-integrated system in the case study can be reduced about 14% in the operating cost and 9% in the capital cost through the strategic import of 20 MWe renewable electricity. The presented case studies fully illustrate economic impacts related to the implementation of renewable electricity to industrial utility systems and demonstrate the benefit of process-integrated optimization for renewable energy integration in practice.

What Factors Cause Weak Industrial Energy Management Practices?

The aim of this study is to investigate the ongoing energy management andenergy efficiency practices in the industries of Peshawar division of KhyberPakhtunkhwa, Pakistan. The outcomes of this research shows that KhazanaSugar Mills, Caliph Pharmaceutical, Naguman Flour Mill have no or lowawareness of energy management practices (awareness level mainly includesbarriers to energy efficiency and drivers for energy efficiency) except FF steelmill. The major paces observed in lacking the awareness are (1) “understand-ing level of staff and workers about energy savings due to energy management practices” (2) “interest level of top managers for energy management imple-mentation” (3) “awareness level regarding energy efficiency” (4) “lack oftechnical competence” (5) “use of standardized procedures”. When theindustry managers were inquired about the relationship with energy serviceproviders it is found that there is absolute absence of energy engineers,relationship with energy consultancies and long-term energy strategy in allthe industries. It was also found that there is lack of using renewable energytechnologies in all industries as they were relying upon using the generatorsduring the blackout hours. In terms of percentage, the energy saving factor of36 kWh/day has also observed with the replacement of fluorescent tube lightswith light emitting diode (LED) tube lights. Proper maintenance and energymanagement practices in the heating, ventilation and air conditioning system(HVAC) would also save energy in a significant amount. The study shows thatthere is an overall improvement factor of 4% to 8% if the inclusion of energymanagement and energy efficiency practices would have been applied in allthe stated industries.

A review on techno managerial approaches to energy optimization in chemical process industries

The chemical process industries, being energy intensive in nature, still struggle to strategically and sustainably consider energy management issues. Energy sustainability depends on a sustainable energy supply, consumption, and waste disposal. This paper analyses current energy management and optimization in industries and also attempts to identify the significance of incorporating energy management into the strategic perspectives of industry through an exhaustive literature review. To ensure the optimization of energy, the paper illustrates the importance of adopting a techno-managerial approach that integrates the technical aspects of energy conservation with relevant management tactics. This is also a preliminary study for proposing a framework for Indian chemical process SMEs to systematically overcome various challenges and seize the opportunity to ensure optimized energy utilization, thereby highlighting the framework’s long- and short-term benefits. The suggestions in the paper would help these industries and local- and national-level policymakers to improve their energy footprint and make the world more energy sustainable.