Optimization Of Energy Consumption Research Articles

Energy‐Aware Power Control Scheme For Water Quality Monitoring Systems

ABSTRACTThis research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments.

Exergy Analysis of a District Heating System with a Predictive Model Based on Neural Network Algorithms

The article presents an exergy analysis of a district heating system (DHS) that incorporates a predictive model based on neural network algorithms. The study focuses on evaluating the thermodynamic efficiency of the DHS when these methods are applied, enabling the prediction of temperature changes and rapid adjustment of system parameters to enhance efficiency. The impact of increasing the number of connected consumers on the exergy efficiency of the DHS with the predictive model (DHPM) is examined. The use of neural network algorithms, such as LSTM, significantly enhances the system's ability to predict external temperature changes and respond promptly, resulting in optimized energy consumption and improved exergy efficiency, particularly at the beginning and end of the heating season. A comparative analysis of the exergy efficiency of traditional DHS and the system with the predictive model is included. The exergy analysis is based on modeling the system’s performance with varying numbers of consumers. The results indicate that an increase in the number of consumers leads to a rise in exergy efficiency due to better energy distribution management. The potential for significant thermal energy savings and reduced operational costs through the use of predictive methods is discussed. The study confirms that applying neural network algorithms in predictive models of DHS can substantially improve efficiency and reliability, leading to a more rational use of energy resources, cost reduction, and minimized environmental impact. The findings support the adoption of these methods in large-scale district heating systems for optimization and enhanced energy efficiency.

Energy Consumption Optimization for an Electric Delivery Vehicle

For nearly two centuries, electric drives have been used in transportation. Nevertheless, they were not always favored by designers. The century-long dominance of heat engines led to the disregard of numerous challenges associated with the operation of electric drive systems. One of these issues is the optimization of energy consumption by an electric vehicle. This publication proposes an electronic Energy Consumption Optimizer (ECO) that predictively uses information about the shape of the route and speed limits on its individual sections to control the motor speed and gear changes in the gearbox. This work presents the structure of the optimizer system and the developed control algorithms. Additionally, electric motor excitation control was used, which may have contributed to reducing the power and weight of the electric drive motor. Simulation studies carried out using WLTP test cycles and cycles from real road routes showed the potential to decrease energy consumption for vehicle movement by approximately 10%.

The Impact of Carbon Emissions Trading Policy on Regional Economy and Pollution Reductions in Chinese Provinces

Carbon emissions trading policy is an important tool to achieve carbon peaking and carbon neutrality goals. In order to explore the effectiveness of carbon emissions trading policy, this paper adopts the difference-in-differences model to analyze the effects of China’s pilot policy on regional economic development and pollution reductions. The results show that carbon trading policy can significantly promote economic development and reduce total carbon emissions, industrial SO2 emissions and solid wastes production of pilot regions. Further research finds that there is significant regional difference in policy effectiveness, with the policy more effective in western areas. In addition to direct effects, carbon trading policy could exert an indirect effect on carbon emissions, air quality, industrial solid wastes and regional economic development by optimizing energy consumption structures and industrial structures and increasing technological investment. This result verifies the “Porter hypothesis”. China should improve the construction of national carbon trading markets, covering more industries, considering the regional differences and negative spillover effect.

Measuring the Effectiveness of the ‘Batch Operations’ Energy Design Pattern to Mitigate the Carbon Footprint of Communication Peripherals on Mobile Devices

The Internet of Things (IoT) is set to play a significant role in the future development of smart cities, which are designed to be environmentally friendly. However, the proliferation of these devices, along with their frequent replacements and the energy required to power them, contributes to a significant environmental footprint. In this paper we provide scientific evidences on the advantages of using an energy design pattern named ‘Batch Operations’ (BO) to optimize energy consumption on mobile devices. Big ICT companies like Google already batch multiple API calls instead of putting the device into an active state many times. This is supposed to save tail energy consumption in communication peripherals. To confirm this, we set up an experiment where we compare energy consumption and carbon emission when BO is applied to two communication peripherals on Android mobile device: 4G and GPS. Results show that (1) BO can save up to 40% energy when sending HTTP requests, resulting in an equivalent reduction in CO2 emissions. (2) no advantages for the GPS interface.

ENHANCING OPERATIONAL EFFECIENCY IN CRUSHERS THROUGH THE USE OF AN INDUSTRY 4.0 BASED CRUSHER CONTROL SYSTEM

In the aggregate and mining industry, an excessive flow rate of raw material from the feeder, caused by irregularities in the raw material being processed by crushers, can lead to blockages or excessive strain on the crusher. Conversely, a low flow rate of raw material can result in high energy consumption by the crusher, despite operating at a low capacity. The issues encountered in the first group result in excessive energy usage in the secondary and tertiary groups. The study focuses on a system that utilizes artificial intelligence and is based on industry 4.0 principles. The system aims to maintain production in the crusher within a specific range by controlling the flow rates of the feeders using an algorithm. This control is done automatically without the need for user intervention. The system optimizes energy consumption while maximizing production capacity and ensures uninterrupted operation. The system was developed during the installation phase at an aggregate pilot plant in the Kahramanmaraş Evri region. It assesses the material capacity data using a belt scale on the crusher, feeder, and output conveyor. This data is then compared to the limit values stored in the database, and the system generates an information signal to initiate the required control actions. Based on this matching result, it sends information to the inverter, coordinates the production cycle, manages and documents the process stages using a structured learning system and artificial intelligence logic.The installation procedure was conducted using two distinct density gradation inputs. As a consequence of the reporting, the records in the report were compared during both the active and inactive states of the system. The project achieved an efficiency of 22% in terms of energy consumption per unit capacity. Based on the whole yearly energy usage, a total of 368609.7 kg of carbon emissions were averted. The facility's aggregate crushing capacity was increased by 40%.

Optimizing energy consumption in post-combustion CO2 capture at an NGCC power plant: a simulation-based approach

Abstract This study aims to optimize energy consumption in post-combustion carbon capture processes at a Natural Gas Combined Cycle (NGCC) power plant. It further addresses challenges associated with steam extraction from NGCC power plants and explores non-condensable steam turbines to boost process efficiency. Various scenarios are explored to minimize energy requirements by leveraging heat integration and alternative solvent compositions. The research uses simulations with commercially available software and literature-derived data to address the challenges of high energy consumption in such processes. Emphasis is placed on optimizing solvent composition and implementing advanced heat optimization strategies to reduce energy consumption. Four scenarios with different solvent compositions are investigated, utilizing Monoethanolamine and aqueous piperazine. The results show that process parameter optimization and heat optimization strategies can result in significant energy savings. To attain a carbon-neutral energy landscape, this research helps develop more sustainable and effective carbon capture methods.

Digitalization of Adjustment and Maintenance Processes for Optimizing Energy Consumption in the Oil and Gas Sector

This article examines the impact of digitalization on adjustment and maintenance processes in the oil and gas sector aimed at optimizing energy consumption. Modern technologies such as big data analysis, the Internet of Things (IoT), and Machine Learning (ML) algorithms are discussed, highlighting how they assist companies in reducing energy consumption and enhancing operational efficiency. Examples of the implementation of these solutions in major companies are provided, demonstrating significant reductions in energy costs and equipment downtime. The advantages of contemporary approaches over traditional adjustment and maintenance methods are emphasized.

Enhancing energy efficiency in Bangladesh’s readymade garment sector: the untapped potential of LED lighting retrofits

Purpose Previous studies emphasized the substantial energy-saving potential of light emitting diode (LED) lighting systems, especially in the clothing industry. However, the specific quantification of energy conservation potential in industrial factories, particularly in Bangladesh’s readymade garment (RMG) sector, remains unexplored. The purpose of this study is to investigate the potential energy savings and efficiency improvements of lighting systems in Bangladesh’s RMG sector using LED technology. Design/methodology/approach Understanding and optimizing energy consumption is crucial in the RMG sector because this sector contributes significantly to the country’s export earnings. For this, an RMG factory was surveyed and possible lighting system retrofitting was estimated and compared. Findings The adoption of energy-efficient lighting options, particularly LED, could decrease the current lighting energy usage from 15% to 7.5% in Bangladesh. First, this study reveals, that the reduction of annual energy consumption was determined to be 18,220 kWh due to the retrofitting of the lighting system with LED tube. Second, it conducts real-time measurements to assess the suitability of in-building lighting systems, providing insights into the current scenario. Lastly, it evaluates the economic and environmental benefits of the proposed lighting system in the RMG industries. Due to the retrofitting of the lighting system, the reduction of equivalent CO2 gas emissions was found to be 119.896 tCO2. Originality/value For the first time, this study explored the potential for enhancing energy-efficient lighting system design through retrofitting in the RMG industry, with a focus on Bangladesh. By addressing these aspects, this study aims to contribute to the advancement of energy efficiency and conservation efforts in the RMG sector, ultimately fostering sustainable industrial development in Bangladesh and beyond.

Empirical insights into India's energy demand: unveiling elasticity dynamics for strategic policy initiatives.

Policymakers and stakeholders can use insights from price elasticity and income elasticities of energy demand to devise targeted interventions that promote energy efficiency, reduce reliance on fossil fuels, and drive the transition to more sustainable and resilient energy systems. This study seeks to examine how changes in income elasticity and price elasticity affect energy consumption over time using the ARDL model and a time-varying parameter technique, state-space Kalman filter. The study's findings reveal that energy prices have a negative and significant impact on energy consumption, whereas GDP per capita and population growth have apositive and significant impact on energy consumption in both the short and long run. The findings underline the importance of economic growth and increasing population in driving growing energy demand in India, which is consistent with the country's energy-intensive development aspirations. The time-varying analysis shows that the price elasticity of energy consumption follows a U-shaped pattern across time. However, from the onset of the new millennium decade, the elasticity of energy consumption with regard to its price has continually decreased, reaching - 0.54 by 2020. Furthermore, the findings imply that the income elasticity of India's energy demand has stabilized around unity, indicating that the country is currently near the middle of the energy intensity-GDP inverted-U trajectory. Given these evolving elasticities, policymakers should take a flexible and forward-thinking strategy to achieve optimal energy consumption levels.

Optimization of marine hybrid power energy consumption

Abstract The energy and power system of the ship is the core of the ship, and the composite energy storage plays a great role in reducing the load power fluctuation of the ship, which effectively optimizes the energy efficiency index and reliability of the ship. [Objective] To better optimize the ship energy management system, a control model is established according to the characteristics of the energy system of hybrid electric ships. [Method] Energy consumption optimization is carried out by using a rule-based energy control strategy and energy control strategy based on equivalent fuel consumption minimum (ECMS). The dynamic control and strategy of the hybrid ship were optimized, and the data simulation and real ship application were carried out on the hybrid experiment platform. [Results] According to the data and results of the real ship application, the following results were shown: The daily fuel saving rate of the ECMS strategy is 14.6% in normal operating conditions and 13.3% in cruise operating conditions. [Conclusion] It has a great effect on the improvement of ship energy efficiency.

Data envelopment analysis and multi-objective genetic algorithm-based optimization of energy consumption and greenhouse gas emissions in rice-wheat system

Data envelopment analysis and multi-objective genetic algorithm-based optimization of energy consumption and greenhouse gas emissions in rice-wheat system

Proximal Policy Optimization with Population-based Variable Neighborhood Search Algorithm for Coordinating Photo-Etching and Acid-Etching Processes in Sustainable Storage Chip Manufacturing

In the complex process of manufacturing storage chips, the photo-etching and acid-etching stages play a crucial role, significantly affecting energy consumption and environmental impact. This paper introduces a novel Bi-Level Programming Model for Storage Chip Manufacturing (BLPM-SCM) aimed at optimizing the coordination between these two stages. The upper-level model focuses on minimizing the time it takes to complete wafer production, while the lower-level model seeks to reduce the number of acid-etching tanks used, thereby balancing production efficiency with resource utilization. To address the inherent complexity of the bi-level model, we present a hybrid meta-heuristic algorithm that combines Proximal Policy Optimization (PPO) with a Population-based Variable Neighborhood Search (PVNS) method. The PPO-PVNS algorithm enhances the intensification phase by adaptively selecting shaking and local search strategies, while PVNS supports the diversification phase, ensuring comprehensive exploration of the search space through iterative updates of the solution population. Extensive numerical experiments demonstrate the algorithm's superior performance and generalization capabilities in optimizing the manufacturing process. It significantly improves the coordination between the photo-etching and acid-etching stages, achieving dual optimization of energy consumption and environmental benefits. Furthermore, this study provides valuable insights and decision-making tools for industry practitioners, offering innovative solutions for scheduling optimization in the semiconductor sector and promoting more sustainable and efficient production practices.

A scientific base for optimising energy consumption and performance in 3D printing

A scientific base for optimising energy consumption and performance in 3D printing

GPT-based data-driven urban building energy modeling (GPT-UBEM): Concept, methodology, and case studies

Achieving carbon neutrality is a critical global goal, with urban building energy modeling (UBEM) playing a pivotal role by providing data-driven insights to optimize energy consumption and reduce emissions. This paper introduces GPT-based urban building energy modeling (GPT-UBEM), a novel approach utilizing GPT’s advanced capabilities to address key UBEM challenges using GPT-4o. The study aimed to demonstrate the effectiveness of GPT-UBEM in performing UBEM tasks and to explore its potential in overcoming traditional limitations. Specifically, (1) basic analytics of urban data, (2) data analysis and energy prediction, (3) building feature engineering and optimization, and (4) energy signature analysis were conducted in four case studies. These analyses were applied to 2,000 buildings in Seoul and 31 buildings in Gangwon-do, South Korea. Through case study, the findings highlighted the ability of GPT-UBEM to integrate diverse data sources, optimize building features for high accuracy in prediction models, and provide valuable insights for urban planners and policymakers through the use of expert domain knowledge and intervention. Additionally, based on the results derived from GPT-UBEM in this study, the current limitations of GPT-UBEM (L1 to L3) and future research directions (F1 to F4) have been outlined.

Environmental and economic assessment of urban wastewater reclamation from ultrafiltration membrane-based tertiary treatment: Effect of seasonal dynamic

This study aimed to assess the environmental and economic performance of an ultrafiltration (UF) tertiary treatment of effluent from an urban wastewater treatment facility. Data from a UF demonstration plant composed of commercially available equipment, including industrial hollow-fiber membranes was used to project a full-scale facility. The results from the demonstration plant recommended different ranges of transmembrane fluxes and sparging air demands under summer and winter conditions to prevent excessive fouling. The energy balance of the full-scale facility would be 0.308 ± 0.112 kWh·m−3 in summer and 0.140 ± 0.040 kWh·m−3 in winter, with blowers' being the main consumers (86–93 %). CAPEX accounted for €0.030 ± 0.002·m−3 in summer and €0.027 ± 0.002·m−3 in winter and membrane acquisition represented 66–69 % of the investment cost. Energy expenditure was the major OPEX cost (66–79 %), with a total operating cost of €0.077 ± 0.023·m−3 and €0.042 ± 0.008·m−3 in summer and winter, respectively. The final average value obtained for the TAC was €0.107 m−3 in summer and €0.068 m−3 in winter. The environmental assessment confirmed optimizing energy consumption and membrane requirements as the main factors influencing environmental sustainability. Specifically, summer and winter emissions of 0.079–0.175 and 0.043–0.079 kgCO2eq·m−3 (Global warming potential); 8.1 · 10−4–1.7 · 10−3 and 4.8 · 10−3–8.1 · 10−3 m3·m−3 (water consumption); 0.019–0.041 and 0.010–0.019 kg oileq·m−3 (fossil fuel scarcity); and 1.4 · 10−4–2.9 · 10−4 and 7.7 · 10−4–1.4 · 10−4 kg Cueq·m−3 (mineral resource scarcity) were calculated, respectively. The obtained permeate quality complied with the most stringent Spanish and EU regulations.

Evaluating sustainability awareness and practices in podiatry clinics in Spain: a path toward a healthy and sustainable future

Background: Sustainability in healthcare is crucial due to the sector's significant contribution to global greenhouse gas emissions. Medical practices must address environmental impact while maintaining high-quality care and equitable access. In podiatry, sustainable practices can reduce the environmental footprint of clinics and improve long-term healthcare outcomes by promoting prevention and resource efficiency. Study design: Cross sectional study Method: We conducted an online survey with 357 practicing podiatrists, all registered with the General Council of Podiatrists of Spain and with at least one year of professional experience. The survey contained 26 questions focused on sustainability in healthcare, podiatry practices, and environmental impact. The questionnaire included both closed-ended questions and Likert-scale items. Responses were scored and categorized into arbitrary sustainability categories: low (<12), medium (12 to 25), and high (>25). Results: Most respondents (82.6%) did not regularly use public transport, and 14% did not engage in any form of physical exercise. More than half of the sample were unaware of the United Nations Sustainable Development Goals (SDGs). Almost all (98%) had not measured the carbon footprint of their clinics, yet 34.7% planned to optimise energy consumption in their clinics. Overall, 41.7% were categorized as low sustainability, 56.9% medium, and 1.4% met high sustainability. Conclusions: Podiatry clinics in Spain demonstrate low sustainability actions and knowledge. Sustainable practice aligns with global environment healthcare urgency, and ensures more responsible podiatric services.. This study provides the first baseline data for this issue, availing comparisons across global podiatric healthcare sectors.

Public Policies for the Energy Efficiency of Buildings in Mexico

In Latin America, the energy crisis has worsened due to the dependence on energy services and fossil fuel imports from highly industrialized countries at prices established by the international market; this is particularly relevant to the construction industry, which presents a significant deficit in optimal energy consumption. Hence, some governments have established public policies to maximize the efficiency of these services and, at the same time, minimize the carbon footprint. In this research study, we reviewed the public policies, strategies, and incentives for energy efficiency (EE) implementation in the residential sector established by the Mexican government. A scoping review methodology was chosen and implemented in the following steps: 1. Research inquiry identification. 2. Determination of the relevant literature and studies. 3. The literature selection. 4. Data graphing. 5. Results collection, overview, and submission. In this systematic review, we identified five mandatory standards (NOM-008-ENER-2001, NOM-009-ENER-2014, NOM-018-ENER-2011, NOM-020-ENER-2011, and NOM-024-ENER-2012), six optional standards, four strategies (Green Mortgage, Integral Sustainable Improvement in Existing Housing, ECOCASA, and NAMA), and three kinds of incentives (green bonds, credit and interest rates (Green Mortgage, FIDE, and Ecocasa), and taxes (Income Tax Reduction)). As a result of the implementation of the above, as of December 2020, NAMA financed 5106 developers of 38 projects in 15 states; contributed to a reduction of 126,779 tons of CO2; and aided 19,913 people. From 2013 to December 2023, EcoCasa subsidized 71,440 households for a total of 224 projects in 25 states; contributed to a reduction of 2.6 million tons of CO2; aided 285,760 Mexicans; and issued EcoCasa certificates for 3,473,556 m2. The results of the EE indicators in residential buildings showed an increase in the housing unit number as well as an increase in household appliances, with those based on power consumption prevailing. The residential sector ranks third in power consumption in Mexico, consuming an estimated 790 pj, of which 76% corresponds to thermal energy and 24% to electric power. Among countries in Latin America and the Caribbean, Mexico has achieved an Energy Transition Index of 62%.

An Adaptive Energy Orchestrator for Cyberphysical Systems Using Multiagent Reinforcement Learning

Reducing carbon emissions is a critical issue for the near future as climate change is an imminent reality. To reduce our carbon footprint, society must change its habits and behaviours to optimise energy consumption, and the current progress in embedded systems and artificial intelligence has the potential to make this easier. The smart building concept and intelligent energy management are key points to increase the use of renewable sources of energy as opposed to fossil fuels. In addition, cyber-physical systems (CPSs) provide an abstraction of the management of services that allows the integration of both virtual and physical systems in a seamless control architecture. In this paper, we propose to use multiagent reinforcement learning (MARL) to model the CPS services control plane in a smart house, with the purpose of minimising, by shifting or shutdown services, the use of non-renewable energy (fuel generator) by exploiting solar production and batteries. Furthermore, our proposal dynamically adapts its behaviour in real time according to current and historic energy production, thus being able to handle occasional changes in energy production due to meteorological phenomena or unexpected energy consumption. In order to evaluate our proposal, we have developed an open-source smart building energy simulator and deployed our use case. Finally, several simulations with different configurations are evaluated to verify the performance. The simulation results show that the reinforcement learning solution outperformed the priority-based and the heuristic-based solutions in both power consumption and adaptability in all configurations.

Role of energy value chain in carbon neutrality: A review

Value chain analysis is an important tool for optimizing operations and decision-making in enterprises. As the concept of sustainable development gains recognition worldwide, research on value chains is increasingly focused on sustainability. Traditionally, energy management and value management have operated in parallel with limited intersections. However, after the 2015 Paris Agreement set the goal of achieving net-zero emissions, carbon management has become integral to national strategies, necessitating a re-evaluation of traditional value chains. In this paper, the “energy value chain” is introduced, a novel concept that integrates energy consumption with value creation and carbon emissions, emphasizing the coupling relationships among “energy flow”, “value flow”, and “carbon flow.” From a review of current value chains in the power, steel, petroleum, and transportation industries, the specific energy value chain for each industry is defined and its rationale and effectiveness are discussed. This integrated analytical method provides a strategic tool for industries or enterprises to optimize energy consumption, reduce carbon emissions, and enhance competitive advantage.