Optimize Energy Use Research Articles

Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning

Additive manufacturing (AM) technologies are growing more and more in the manufacturing industry; the increase in world energy consumption encourages the quantification and optimization of energy use in additive manufacturing processes. Orientation of the part to be printed is very important for reducing energy consumption. Our work focuses on defining the most appropriate direction for minimizing energy consumption. In this paper, twelve machine learning (ML) algorithms are applied to model energy consumption in the fused deposition modelling (FDM) process using a database of the FDM 3D printing of isovolumetric mechanical components. The adequate predicted model was selected using four performance criteria: mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It was clearly seen that the Gaussian process regressor (GPR) model estimates the energy consumption in FDM process with high accuracy: R2 > 99%, EVS > 99%, MAE < 3.89, and RMSE < 5.8.

Internet of Things (IoT) adoption challenges in renewable energy: A case study from a developing economy

Digitalization of Renewable Energy (RE) systems will play a decisive role in integrating clean energy sources and optimizing energy use. While energy digitalization is mainly governed by technological progress, the societal acceptance of such emerging technologies is essential for a successful energy transition. This study aims to identify critical challenges to IoT-enabled renewable energy use through the lens of the consumers. It attempts to analyze the challenges to implementing IoT effectively in emerging economies like India. The present study employs Exploratory Factor Analysis (EFA) to classify 16 critical challenges into five main dimensions, using the responses obtained from 95 participants. Subsequently, the fuzzy decision-making trial and evaluation laboratory (DEMATEL) prioritizes the identified challenges and organizes them into cause-and-effect groups based on their interactions. The findings indicate that technological challenges constitute the most critical cause barrier that affects other category barriers. Besides technology, challenges reflecting the social infrastructure have emerged prominently in this study. Thereafter, the analysis suggests that modifications to the structure, procedures, and technology are required to create capabilities and enhance the compatibility of IoT with RE applications. Further, proactive governance and reorienting market design are highlighted to foster emerging technologies and develop a robust regulatory framework. The insights from this study are pertinent for policymakers, regulatory bodies, and practitioners seeking to capitalize on the immense potential of this sector and expedite energy transactivity.

ASSESSING LAND SURFACE TEMPERATURE IN URBAN AREAS USING OPEN-SOURCE GEOSPATIAL TOOLS

Abstract. Land surface temperature (LST) in urban areas can be traditionally observed by thermal remote sensors. The increasing availability of 3D city models provides an alternative approach based on geospatial modeling. Using solar radiation tools and scripting in GRASS GIS, we have developed a physically-based LST model that can be used to estimate LST in urban areas represented by vector 3D city models. It uses standard input parameters such as solar irradiance, albedo or convection heat transfer coefficient for urban surfaces. The solar irradiance is estimated using the v.sun solar radiation add-on module in GRASS GIS. The LST values are calculated using map algebra operations using a Python script. The suggested methodology was applied to the study area in the city of Košice, Slovakia. Results indicate that urban morphology has a strong impact on spatial distribution of LST during the daylight hours. The accurate parameterization of urban surfaces can increase the accuracy of the model that can be used for urban planning, optimization of energy use in buildings or mitigation of urban heat island effects.

The molecular basis of cereal grain proteostasis.

Storage proteins deposited in the endosperm of cereal grains are both a nitrogen reserve for seed germination and seedling growth and a primary protein source for human nutrition. Detailed surveys of the patterns of storage protein accumulation in cereal grains during grain development have been undertaken, but an in-depth understanding of the molecular mechanisms that regulate these patterns is still lacking. Accumulation of storage proteins in cereal grains involves a series of subcellular compartments, a set of energy-dependent events that compete with other cellular processes, and a balance of protein synthesis and protein degradation rates at different times during the developmental process. In this review, we focus on the importance of rates in cereal grain storage protein accumulation during grain development and outline the potential implications and applications of this information to accelerate modern agriculture breeding programmes and optimize energy use efficiency in proteostasis.

A case study on long-term performance optimization of a geothermal heating system

The optimal use of geothermal energy necessitates the performance evaluation and economic analysis of the geothermal heating systems. This work presented a field study of the long-term operating performance of a geothermal heating system in Xixian New Area, China. Nine deep coaxial ground heat exchangers (GHEs) with depths of 2,500 m were adopted for space heating to the residential buildings, with a total heating area and heating load of 136,097.71 m2 and 6,082 kW, respectively. The system’s 30-year running performance at various volumetric flow rates and operating modes was simulated. Through response surface analysis and multi-objective optimization, the levelized cost of energy (LCOE) and payback period were obtained based on the thermal performance analysis. It is concluded that the operating parameters have a significant impact on the system economy. When the daily operating time reduces from 24 hours to 8 hours, the payback period will be lowered from more than 25 years to approximately 11 years. When the volumetric flow rate is 35 m3·h-1, the examined system achieves the lowest LCOE. The minimum values of LCOE are 13.2 $/GJ, 11.6 $/GJ, 9.4 $/GJ, 7.8 $/GJ and 5.6 $/GJ when the system operates 24 hours (continuous operation), 20 hours, 16 hours, 12 hours and 8 hours a day, respectively. With the optimal flow rate, the average heat exchange rate of the single GHE increases from 295 kW to 519 kW after 30 years of operation when the daily operating time is reduced from 20 hours to 8 hours. The proposed method and findings can be used to guide the high-efficiency operation, which is conducive to reducing operating costs of geothermal heating systems.

Optimization of energy use and academic performance for educational environments in hot-humid climates

Thermal comfort and air quality are important factors for indoor environment in school buildings owing to the strong relationship with students’ academic performance. However, the expense of achieving expected environment quality in classrooms is unaffordable by many schools; thus, this study aims to suggest optimal strategies for balancing energy use and indoor environment quality simultaneously. Through the field measurement and building energy simulation, we analyzed the influences of temperature set-point and ventilation rate on environment quality and clarified the optimal trade-off measures for Taiwanese primary school. The optimal combination of set-point and ventilation rate considered academic performance and cooling energy based on single-objective optimization is proposed and revealed the highest hourly average cost performance is 2.13%/kWh each hour in Taipei and 2.03%/kWh each hour in Kaohsiung, which respectively corresponding to the set-point and ventilation rate of 24 °C and 26 °C, 12.6 L/s-person and 22.05 L/s-person. Moreover, the optimal combinations concerning financial constraints are also confirmed by Pareto front through multi-objective optimization. The results indicated that to achieve the academic performance higher than 85% in primary school, the optimal solution sets are scattered between the set-point of 23 °C–25 °C and the ventilation rate of 20 L/s-persoñ25.2 L/s-person. Consequently, this study provides recommendations for policymakers to formulate strategies to maximize the academic performance in hot-humid climate zone.

Prospective environmental assessment of reprocessing and valorization alternatives for sulfidic copper tailings

Waste from primary mining operations, especially mine tailings, receive much attention as potential secondary resources that can transform liabilities into resources. The primary intention is to minimize mine tailings disposal problems through volume reduction while recovering secondary resources for industrial materials. However, the environmental benefits and tradeoffs behind these objectives remain unclear. This study conducts a process-based life cycle assessment (LCA) study to investigate multiple reprocessing pathways of copper tailings to co-produce secondary metals and building materials. Four design options representing different value-recovery routes are constructed to assess the environmental burdens of reprocessing chains and the associated benefits from displaced virgin products. This study assesses emerging technologies in a prospective LCA, with projections of bottom-up foreground process modeling and background data like energy supply scenarios. Our analysis reveals that the assessed technologies can only be beneficial when all co-products are utilized. Results indicate that reprocessing may save from 25 up to 930 kg CO2-eq per tonne of treated copper tailings. Potential environmental benefits depend on the reprocessing routes, technology upscaling parameters, and quality of secondary products. We propose recommendations to enhance the environmental performances of mine tailings reprocessing strategies, such as switching to low-impact chemical alternatives and optimizing energy use. Our findings can support the sustainable development of the metal industry in terms of waste management and secondary resource use.

Biological nitrate removal from a drinking water supply with an aerobic granular sludge technology: An environmental and economic assessment

Nitrate pollution of groundwater, mainly from agricultural applications, is a widespread water quality problem in many countries. The aim of this study was to identify and compare the main environmental impacts and costs associated with removing nitrate from groundwater under a conventional treatment technology and a technology based on a biological treatment process (denitrification using aerobic granular sludge). The analysis focused on the first real-world experience of an industrial-scale implementation of said technology, applied to the drinking water supply in a small town in Spain. The methods selected for the environmental and economic evaluations were life cycle assessment and cost-effectiveness analysis, respectively. The drinking water treatment technologies under study were a conventional reverse osmosis plant and a plant using a biological treatment called ecogranularwater. The analysis of these two drinking water production processes was divided into two phases: structure and water treatment. This study demonstrates that the biological technology produces drinking water in a more environmentally-friendly, cost-effective way, and with lower energy costs. The greatest environmental impacts from the reverse osmosis technology occurred in the water treatment phase due to the high levels of energy consumption (up to 1.68 kWh m−3 higher than with the ecogranularwater technology). In the structure phase, the biological technology contributed more than reverse osmosis in all impact categories, with values ranging from 91% in freshwater ecotoxicity to 98% in stratospheric ozone depletion. The cost of producing 1 m3 of water was estimated as 43% lower with the ecogranularwater technology compared to reverse osmosis. In order to further lessen the environmental impact of the biological technology, efforts should be focused on reducing and optimizing energy use and making improvements to the design of the structure. This biological technology proved a good alternative for small and medium-sized municipalities with problems of nitrate-polluted water. The decision to apply technological innovations in drinking water treatments to remove nitrates can be supported by environmental and economic studies.

Energy efficiency measures for oil rig operations

Energy efficiency is a crucial focus in oil rig operations as the industry strives to reduce energy consumption and minimize environmental impact. This review explores various energy efficiency measures that can be implemented on offshore and onshore oil platforms, highlighting key technologies and strategies for enhancing operational sustainability. Electrification of oil rigs, replacing traditional diesel generators with grid power or renewable energy sources such as wind or solar, is identified as a significant step toward reducing reliance on fossil fuels and lowering CO2 emissions. Additionally, the use of heat recovery systems is emphasized for capturing waste heat from industrial processes and converting it into usable energy, thereby enhancing overall energy efficiency. Advanced monitoring and control technologies also play a critical role in optimizing energy use. By utilizing real-time data collection and automation, platforms can monitor energy consumption patterns, make data-driven adjustments, and implement predictive maintenance systems to reduce energy waste. The review further examines the use of energy-efficient equipment, including advanced motors, pumps, and compressors, as well as the potential of integrating renewable energy sources like solar and wind power into oil rig operations. Key challenges such as high upfront costs, technical constraints, and harsh environmental conditions are discussed, along with potential solutions such as government incentives, industry collaboration, and technological innovation. The findings suggest that implementing these energy efficiency measures will not only reduce the carbon footprint of oil platforms but also result in significant cost savings and align with global sustainability goals. The review concludes by underscoring the need for ongoing investment and policy support to drive further advancements in energy efficiency in the oil and gas industry.

A weighted Markov-clustering routing protocol for optimizing energy use in wireless sensor networks

Interest has increased in wireless sensor networks (WSNs) in fields such as healthcare, industrial control and environmental monitoring. More recently, WSNs have been widely deployed in Internet of Things (IoT) based applications. They are considered as an essential part of IoT networks. The expanded use of WSNs raises daunting technical challenges, not the least of which is sensor battery energy conservation. The design of efficient sensor clustering strategies to reduce energy consumption by data transmission throughout the WSN has become crucial. Several applications of WSNs induce a random deployment of sensors such as the monitoring of conflict zones, the study of natural phenomena in hostile zones or rescue operations. In this context, energy conservation should be ensured according to the non-uniformity of the resulting sensor distribution in different areas of the network. In this study, a novel weighted Markov clustering protocol that considers sensor abundance for cluster-head and queried sensor selections is presented. The new protocol aims to decrease intra-cluster energy consumption by reducing the sending of redundant data in sensor-dense regions. In addition, it attempts to prolong sensor-sparse regions lifetime by limiting the number of queried sensors. This protocol combines a Markov clustering of sensors with a sensor weighting based on residual energy and sensor abundance in the network. The proposed protocol is a significant improvement of an existing unweighted Markov clustering protocol. The unweighted Markov clustering protocol is based on sensor residual energy and sensor location without taking into account the sensor abundance in different areas of the network. Simulations affirm that the new protocol handles more appropriately the non-uniformity of sensor distribution and enhance the durability of wireless sensor networks. Indeed, simulation results show that the proposed clustering protocol outperforms its unweighted ancestor and other well-known clustering protocols in terms of energy conservation and network lifetime. The number of expired sensors and the average dissipated energy are reduced, whereas, the average sensor lifetime is prolonged compared to the unweighted ancestor, or HEED, LEACH, PEGASIS, and TEEN.

Circular economy of waste in agrifood supply chain: A review

AbstractThrough a systematic literature review (SLR), this study aims to analyze the progress achieved in international literature regarding the circular economy (CE) of food waste. As a result, we realized that most of the authors considered had proposed different mathematical models, chemical and thermal treatment methods, statistical, and conceptual frameworks, besides that quantitative and qualitative studies to explain the processes related to the waste and CE in the agrifood sector. Furthermore, in business, management, and accounting, the CE of food waste focuses on added value and optimizing energy use rather than approaching the principles of reduction, reuse, and recycling. This study constitutes a first attempt at understanding the CE of food waste in business, management, and accounting fields of knowledge. This SLR can assist decision‐makers, managers, and governments to formulate policies and strategies to effectively implement CE in agrifood supply chains.

Rateworkspace: BIM integrated post-occupancy evaluation system for office buildings

The feedback obtained from occupants regarding their comfort needs and performance of buildings is critical for assessing occupant satisfaction, identifying the operation and maintenance (O&M) issues in time and for improving resource efficiency in buildings. Current facility management (FM) systems and occupant feedback collection practices, however, have limitations in supporting effective decision-making in FM, as they lack the necessary contextual data related to the occupant feedback (e.g., building geometry, systems, elements). Building Information Modeling (BIM)-enabled FM systems are used for combining different types of FM information with building models; however, occupant feedback is still not effectively utilized in FM since it is not integrated with BIM. In this study, a BIM integrated post-occupancy evaluation system prototype is developed for: (1) collecting occupant feedback along with the contextual information related to the feedback items in a structured way, and (2) presenting this information as integrated with BIM to the facility managers. This enables conducting spatio-temporal queries and supports effective decision-making by visualizing the collected feedback. The prototype was designed by using qualitative shadowing with FM teams to identify information needs and use case analysis to determine how contextual data integrated with BIM could be collected from office occupants who are non-technical persons with limited information on building models. This paper identifies the FM query categories that are required to process the occupant feedback and describes the RateWorkSpace prototype developed for office buildings. The deployment of the prototype in a real-world office demonstrates that the proposed system is applicable, practical, usable, and that real-time building performance data can be both collected and analysed with the developed system. This has the potential to increase the effectiveness of the FM and O&M processes, and help to create office spaces with optimized energy use and occupant comfort that also supports occupant well-being and productivity.

Optimal energy management strategy for a renewable‐based microgrid considering uncertainty

ABSTRACT Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively.

ECMS: An Edge Intelligent Energy Efficient Model in Mobile Edge Computing

With the increasing popularity of mobile edge computing (MEC) for processing intensive and delay sensitive IoT applications, the problem of high energy consumption of MEC has become a significant concern. Energy consumption prediction and monitoring of edge servers are crucial for reducing MEC’s carbon footprint in accordance with green computing and sustainable development. However, predicting energy consumption of edge servers is a nontrivial problem due to the fluctuation and variation of different loads. To address this problem, we propose ECMS, a new edge intelligent energy modeling approach that jointly adopts Elman Neural Network (ENN) and feature selection to optimize the consumption of energy on edge servers. ECMS considers 29 parameters relevant to edge server energy consumption and uses the ENN to develop an energy consumption model. Unlike other energy consumption models, ECMS can successfully deal with load fluctuation and various sorts of tasks, such as CPU-intensive, online transaction-intensive, and I/O-intensive. We have validated ECMS through extensive experiments and compared its performance in terms of accuracy and training time to several baseline approaches. The experimental results show the superiority of ECMS to the baseline models. We believe that the proposed model can be used by the MEC resource providers to forecast and optimize energy use.

Multi-Objective Optimization and Test of a Tractor Drive Motor

The design objectives of the structural parameters of the tractor drive motor are diverse, and the constraints are complex. It is difficult to optimize the overall performance of the unit by using the empirical method and single-objective optimization method. This paper proposes a multi-objective optimization method for tractor drive motors based on an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II). Constraints are formulated according to the inherent characteristics of the motor itself and the characteristics of the tractor’s working conditions. The objective function was established with the heat loss of the drive motor and the total efficiency of the drive system. Based on the designed solution process of NSGA-II algorithm, an example optimization was carried out, and the tractor electromechanical drive system was carried out with the single-objective optimization results of the optimal energy use efficiency of the drive motor and the optimal mechanical transmission efficiency of the transmission system as the control group. The test results show that compared with the control group, the proposed multi-objective optimization method can make the overall tractor system efficiency the highest, and the maximum and rated values of the total efficiency ηq of the drive system of the multi-objective optimization design scheme. Compared with the optimal design scheme with ηme as a single objective, it was increased by 2% and 1.4%, respectively, and compared with the optimal design scheme with ηtr as a single objective, it is improved by 26.5% and 73.6%, respectively. It can provide an effective calculation method for the motor design problem in the subsequent development of the tractor electromechanical drive system.

Use and cost optimization for underground mines electrical energy: A case of a mine in Zvishavane

Mines profit margins are being lowered by high energy tariffs, demand costs, energy costs due to high energy consuming equipment and energy losses. To mitigate the problem of higher energy cost, mines should optimize energy use centred on productive mining equipment and peak demand shift since meaningful reduction of energy cost is on productive energy consumption. A model of the mining electrical energy use and costing was produced to provide a detailed mining industry research study since other studies were mainly centred on energy optimization reviews and single parameter analysis. The mine energy use and cost optimization were based on rescheduling of shifts and equipment, the proposed mine operation model, load crippling, the power factor correction and the implementation of the renewable energy supply coupled with noted parameters. The research established that a non-renewable energy supply combination of the rescheduling methods, load crippling, power factor correction and recommended mine operation mode is the best way of reducing energy cost of 21.81% considering the Net Present Value (NPV). The recommended mine operation mode guarantee reduced energy costs by 8.07%, enable continuous production during maintenance time, increased mine asserts and increased mine production capacity when higher production is needed.

A multi-facet BIM based approach for Green Building design of new multi-family residential building using LEED system

Pakistan’s as developing economy is in crisis due to rising energy and water demand. This study considers the Green Building concept as it accounts for optimum use of energy and water conservation. A building information modelling approach has been used to analyze the Green Building concepts. Parameters of Green Building are defined within Leadership in Energy and Environmental Design rating system. A parametric model of the case study building was developed on the Autodesk Revit platform based on original drawings and details. The same model was then subjected to change by provision of insulation measures. The energy use intensity of both models is calculated and analyzed. The results from the study suggest that if better measures and decision are taken in design stage, there is potential to conserve energy by approx. 28% and water by approx. 32% and subsequently can produce renewable energy by approx. 15%. For the validation of model and results, a comparative study was made on two other platforms, namely, Green Building Studio and Insight. The study further suggests a payback period of approx. 2.3 years after the cost and saving analysis. This integrated approach expedites the certification process thus saving significant time and resources.

Live Life Cycle Assessment Implementation using Cyber Physical Production System Framework for 3D Printed Products

Sustainable manufacturing aims to deal with the challenges such as climate change, biodiversity loss and resource scarcity by minimising the environmental impacts due to product, processes, and systems through optimal use of energy and resources. Life cycle assessment (LCA) has become an important tool to identify, evaluate and assess the environmental impact of a product, process, or system, along with all the stages of the life cycle of a product. However, several drawbacks such as complexity, uncertainty and impreciseness are also associated with this methodology. Therefore, live LCA as a plausible solution, is gaining popularity with the advancements in Industry 4.o tools and techniques, enhancing ability to collect and analyse live data from various processes, interpret results, identify hotspots, trade-offs, and present better ideas about the environmental impacts in-line with the process. This paper proposes a cyber-physical production system framework for live LCA to estimate the environmental impact of 3D printed products with different combinations of design and process parameters. Also, optimum settings of these parameters are determined, and validated to minimize the environmental impacts. This enables real-time monitoring of the environmental impacts, driving prompt decision support for useful insights to the operator, project manager, business manager, or customer thereby improving the visibility and transparency.

Enclosed Transitional Space Parameters for Optimizing Energy Use in in Hot and Arid Climate Using Uncertainty and Sensitivity Analysis

Enclosed Transitional Space Parameters for Optimizing Energy Use in in Hot and Arid Climate Using Uncertainty and Sensitivity Analysis

MAIN TRENDS OF SUSTAINABLE DEVELOPMENT OF THE RESTAURANT ECONOMY

Economic growth and sustainable development of the state are impossible without an urgent reduction of anthropogenic impact on the environment through changes in the production and consumption of goods and resources. The Covid-19 pandemic not only weakened the economy, exacerbated social problems, but also affected the tastes and attitudes of consumers, causing some reaction from restaurateurs. Certain trends have already been identified that will promote the growth of the restaurant industry in the context of sustainable development based on the optimal use of energy, raw materials, labor, etc. The Covid-19 pandemic not only weakened the state’s economy, exacerbated societal problems, and adjusted the correctives in forming of tastes and attitudes of consumers, but caused some reaction from restaurateurs. Certain trends have already emerged that contribute to the growth of the restaurant industry in the context of sustainable development, which is based on improving the optimal use of energy, raw materials, labor, etc. The purpose of the article is to highlight current and future trends in the restaurant industry, implemented on the basis of analysis of existing problems and consumer demands. Among the trends of sustainable development of the restaurant industry are such as: reduction and transparency of supply chains; greening of technological processes; reduction of garbage and waste; reduction of raw material losses, saving water and energy consumption; Healthy Eating; increasing the position of vegan dishes on the menu; including vegetable substitutes of meat; personalization and personification of services; growing demand for delivery services; automation of production and management processes.