Improve Energy Savings Research Articles

The Impact of Hydrogen on Flame Characteristics and Pollutant Emissions in Natural Gas Industrial Combustion Systems

To improve energy savings and emission reduction in industrial heating furnaces, this study investigated the impact of various molar fractions of hydrogen on natural gas combustion and compared the results of the Non-Premixed Combustion Model with the Eddy Dissipation Combustion Model. Initially, natural gas combustion in an industrial heating furnace was investigated experimentally, and these results were used as boundary conditions for CFD simulations. The diffusion flame and combustion characteristics of natural gas were simulated using both the non-premixed combustion model and the Eddy Dissipation Combustion Model. The results indicated that the Non-Premixed Combustion Model provided simulations more consistent with experimental data, within acceptable error margins, thus validating the accuracy of the numerical simulations. Additionally, to analyze the impact of hydrogen doping on the performance of an industrial gas heater, four gas mixtures with varying hydrogen contents (15% H2, 30% H2, 45% H2, and 60% H2) were studied while maintaining constant fuel inlet temperature and flow rate. The results demonstrate that the Non-Premixed Combustion Model more accurately simulates complex flue gas flow and chemical reactions during combustion. Moreover, hydrogen-doped natural gas significantly reduces CO and CO2 emissions compared to pure natural gas combustion. Specifically, at 60% hydrogen content, CO and CO2 levels decrease by 70% and 37.5%, respectively, while NO emissions increase proportionally; at this hydrogen content, NO concentration in the furnace chamber rises by 155%.

Energy Management for Smart GDS with Hybrid AC/DC Microgrid and Renewable Energy: SCO-GBDT Approach

This manuscript proposes a hybrid method for energy management (EM) of a solar photovoltaic (PV) hybrid microgrid (MG) for the residential distribution system (DS). The proposed approach integrates the single candidate optimizer algorithm (SCOA), and gradient boost decision tree algorithm (GBDT), called the SCOA-GBDT algorithm. The main contribution of this manuscript is to (a) effectively achieve battery storage, solar PV, and loads to improve energy savings and lessen the loss of conversion; (b) effectively handle solar photovoltaic, battery storage, and loads to recognize cost-effective power distribution using the proposed technique; and (c) effectively handle weak photovoltaic power prevalent in the DC side of MG by an auxiliary-battery arrangement to preserve energy. The proposed energy management strategy lowers the conversion losses in the residential DS. Here, the dc loads are provided by a solar photovoltaic, the utility-grid provides the AC loads, and an auxiliary-battery bank is considered for storing the energy. Then, the performance of the proposed technique is done in MATLAB software and is compared to different existing approaches. From the simulation outcome, it is concluded that the proposed approach reduces costs and losses compared to the existing approaches.

A composite indicator-based energy-efficiency benchmarking for residential buildings

PurposeBuildings require significant energy, and meeting energy demands is becoming exceedingly challenging. Energy demand reduction goals are now prioritised as the demand is rising. Energy-saving improvements and opportunities can be provided if enough information is provided through building energy benchmarking. The study focuses on developing a framework for benchmarking the energy efficiency of residential buildings.Design/methodology/approachThis study applied multiple linear regression analysis to analyse the energy use of residential buildings and establish energy benchmarks. Over 2000 data from Jaipur city were surveyed, and regression analysis was done on 1527 datasets after fundamental statistical analysis. The research considered the significant energy used by household appliances and placed a greater emphasis on end-use appliances.FindingsThe comparison of the developed framework with the standard rating plan was carried out to evaluate the accuracy of the benchmarks. The validation of the model determines the gap between the predicted and actual value of the building energy. The recommendations were made for organisations and policymakers to employ multiple or combinations of methods to assess the reliability of the developed benchmark framework.Practical implicationsPolicymakers may promote awareness campaigns encouraging homeowners to consume less energy and make buildings more energy efficient. This technique may be applied worldwide with the proper and suitable adjustments and information provided.Originality/valueTo our knowledge, India needs residential building energy benchmarking framework studies. In addition, a new framework based on Composite Indicators was implemented to overcome the scepticism of the EPI/BPI or floor-based approach held by several academics and to offer energy benchmarking for residential buildings.

Optimization of solar-air source heat pump hybrid heating system

Abstract In order to construct a versatile, complementary, low-carbon, clean, and efficient heating system, this study explores the potential of a solar-air source heat pump (SAHP) hybrid heating system. A simulation model is established in TRNSYS, and the particle swarm algorithm (PSO) and genetic algorithm (GA) from GENOPT and MATLAB are employed. With the aim of minimizing the annual cost, optimization is conducted on the key parameters and operational strategies of the system. Comparative analysis between the two algorithms reveals that PSO slightly outperforms in cost reduction, while GA exhibits a slight advantage in enhancing system performance. Significantly improved energy savings are achieved by appropriately reducing the rated heating capacity of the air source heat pump and increasing the volume of the water tank.

Empowering Unskilled Production Systems Consultants through On-the-Job Training Support: A Digital Triplet Approach

This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement process knowledge of skilled production systems consultants, facilitating imitation by unskilled consultants. We adopted the Digital Triplet (D3) concept, an extension of the authors’ digital twin framework to intelligent activities, aligning with our study objectives. Recognizing the difficulty and inadequacy of knowledge transfer in production systems consulting OJT, we propose an OJT support method integrating a decision-making modeling approach for skilled consultants’ processes based on the Generalized Production Systems Consulting Process Model (GCPM) from prior literature into traditional OJT methods involving self-learning and direct instruction. This method enables the construction of a domain-specific GCPM, formalizing the improvement process flow implemented by skilled consultants and linking it to production improvement expertise and tools. In a case study focused on energy-saving improvement, we constructed and tested a domain-specific GCPM’s efficacy in facilitating the transfer of difficult-to-transfer knowledge. The results indicate that domain-specific GCPM facilitates such knowledge transfer, including specialized improvement, knowledge utilization, rationale, and adaptation to specific cases.

Impact of Real-Time Blind Slat Angle Control on Reducing Total Energy Consumption of a Library Building Using an Optimized Artificial Neural Network Model: A Case Study of the Six Moroccan Thermal Zones

Abstract The prevalence of extensive glazed areas in contemporary buildings contributes significantly to solar radiation infiltration, elevating energy demands and causing discomfort for occupants. Window shading devices play a pivotal role in addressing this challenge. This paper presents the development and optimization of an artificial neural network (ANN) predictive model, designed to enable real-time control of slat angles by predicting total energy loads, specifically during summer (for cooling and lighting purposes). The refined model demonstrates high precision, achieving a normalized root mean square error (nRMSE) of approximately 1.72% and a correlation coefficient (R) of around 0.999, despite utilizing limited meteorological data. Key inputs for the model include solar radiation, solar altitude, and external temperature, with a particular focus on slat reflectivity. The study assesses the efficiency of three slat types based on their reflectivity: high (80%), medium (50%), and low (20%). Additionally, the research explores the impact of window-to-wall ratio (WWR) values on the control system's efficacy, revealing a positive correlation between higher WWR values and improved energy savings through ANN slat angle control. Furthermore, the study extends the applicability of the ANN model to the six thermal zones in Morocco, affirming its generalization across diverse environmental conditions.

Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

<p>In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation.</p> <p> </p>

Role of silicon carbide in enhancing microwave hybrid heating and reducing dewaxing time in investment casting

Mould cracking is a common problem in investment casting (IC) that occurs during the dewaxing process, which is caused by the difference in thermal expansion between the wax pattern and the ceramic mould. Therefore, appropriate modifications to the mould, waxes and hybrid heating of the materials are proposed to address this problem. This study aims to investigate the effects of silicon carbide (SiC) addition on dewaxing time and mould structural defect in two different waxes (HYFILL B289 MOD S and SIVUCH L1203) under microwave hybrid heating. For the mould fabrication, stucco (Al2O3-SiO3) was added with various amounts of SiC to improve microwave heat absorption capabilities. The modified stucco was applied on layers 3–6 to ensure that the mould was more durable and suited for the casting process. The thermal expansion of the waxes and the ceramic mould was evaluated to determine its thermal expansion mismatch. The density, porosity, thermal conductivity, and dielectric properties were characterized to evaluate the properties of modified green mould. The results showed that SIVUCH L1203 wax outperformed HYFILL B289 MOD S wax, in obtaining a crack-free ceramic investment mould with a clean internal surface. This superiority is attributed to the exceptional thermal and absorption properties of the SIVUCH L1203 wax. Furthermore, the incorporation of SiC played a pivotal role in enhancing the hybrid heating process while maintaining the density and porosity of the green moulds. This was evident by the remarkable reduction of dewaxing time by 62% with 7.5% of SiC addition, which also contributed toward improving energy savings and sustainability.

Pneumatic Fault Monitoring and Control for Sustainable Compressed Air Systems

Sustainable development in the industrial sector has been widely explored in recent years, to achieve a carbon-neutral industry. Compressed air systems are widely used in the industrial sector. Numerous energy-saving improvements have been studied within this scope, as leaks make these systems inefficient. Nevertheless, it has yet to be seen how different optimisation techniques can be utilised to mitigate fault effects. This study shows how fault monitoring was performed on a multi-actuator system, using different time domain indicators including, mean and standard deviation. This was followed by exploring the system behaviour when pressure and flowrate control strategies were executed to minimize fault impacts. Fault monitoring, using the indicator data, was successful. For instance, as a 1 mm leak was induced and the consumption increased by 34%, the standard deviation in pressure drop reduced by 6% and the mean actuation time decreased by 13%. Though the mean in pressure drop was useful for fault monitoring other pressure indicators, including standard deviation, provided additional monitoring capabilities. During this monitoring exercise, it was also found that improved sensor accuracy resulted in less reading variations, obtaining more conclusive results and identifying faults of smaller sizes. As faults were accurately identified and characterised, it was then possible to mitigate their effects via pressure and flowrate adjustments. Results proved promising, as both contributed to air consumption decreases of 16% and 11%, respectively. Although such modifications decreased the production rate by 2-5%, the previously mentioned savings outweighed this decrease. This work highlights the need for development of fault monitoring and control systems which maintain the productivity and energy performance of pneumatic systems.

Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices.

Numerical heat transfer study of square duct equipped with novel flapped V-baffles

The paper describes a computational study of heat transfer enhancement inside a square duct with V-shaped flapped baffles located repeatedly on the bottom and top walls for fluid flowing with Reynolds numbers (Re) from 3000 to 21,000. The basic goal of this work is to attain the largest relative Nusselt number (Nu/Nu0) whilst maintaining the highest thermal performance to improve energy savings. A finite volume method was used in the computations, along with the Realizable k‒ε turbulent model. The variable baffle parameters considered first in the current simulation were the relative height/blockade ratio (BR = 0.05−0.2) and the flap angle of the baffle hole (β = 0° − 90°), while the fixed parameters included the attack angle (α = 60°), hole diameter ratio (dR = 0.5), and pitch ratio (PR = 0.5). To accomplish this goal, the previously mentioned parameters providing the best thermal performance were investigated further by extending the values of BR to 0.25−0.3, dR to 0.8 and α to 45°−30°. The simulation results indicate that the jet flowing from the flapped hole, as well as the vortices created by the baffle, can boost heat transfer and friction loss in comparison to the plain duct. In comparison, using a flapped baffle with β > 0° results in less friction loss, a greater thermal enhancement factor (TEF), and a higher Nusselt number than using a baffle with no flap. The first investigation disclosed that for BR = 0.2 and β = 20°, the greatest TEF of 2.19 with Nu/Nu0 of 7.9 times are obtained. The extended study, on the other hand, showed that the highest TEF of roughly 2.49 with Nu/Nu0 of 8.4 times are seen for α = 45°, dR = 0.8, BR = 0.25 and β = 20° at lowest Re. Thus, the flapped baffle provides a significant increase in Nu/Nu0 and TEF over the baffle alone.

An example analysis of energy-saving improvement based on air compressor inlet pre-treatment

Briefly introduced the basic principles and characteristics of air compressor operation, and some commonly used air compressor energy-saving operation measures in industrial enterprises; The current situation of air compressor system of Ningbo cigarette factory was analysed, and the impact of different outdoor temperature and humidity conditions on air compressor energy consumption was analysed in winter and summer. Based on the existing conditions of the factory, the fresh air intake room of the air compressor is optimized and upgraded. A double-inlet air duct system is adopted. The indoor and outdoor air enthalpy value can be automatically calculated through the temperature and humidity sensor and central control system. The air compressor intake air is pre-treated according to the results, making full use of the outdoor low enthalpy air and the existing artificial cooling source in the factory area to improve the operating efficiency of the air compressor.

Numerical study of two-axis adaptive HVAC vents in developing uniform thermal comfort and improving energy savings

Numerical study of two-axis adaptive HVAC vents in developing uniform thermal comfort and improving energy savings

Optimizing the Growing Dual Credit Requirements for Automobile Manufacturers in China’s Dual Credit Policy

Dual credit policy (DCP) is a market-based mechanism introduced by the Chinese government to promote the new energy vehicle (NEV) industry and improve energy savings in China. To offer sufficient impetus for the NEV industry while providing sufficient transitional buffer time for automobile manufacturers (AMs), the government needs to scientifically design how to gradually increase its dual credit requirement for AMs year by year. To assist the multi-year DCP design, this paper proposes a generalized Nash equilibrium model to predict AMs’ short-term decisions (i.e., vehicle production and credit trading) and long-term decisions (i.e., investment in production capacity expansion and research and development) under any DCP, considering the interactions among AMs’ decisions, vehicle prices, and credit price. Based on the equilibrium model, we then develop a bi-level programming problem to optimize the multi-year DCP. With numerical experiments, we show that implementing the optimal DCP can effectively enhance the market share of NEVs. In the context of the optimal multi-year DCP, the credit requirements set by the government should maintain a relatively low threshold during the initial years, but rise rapidly after that. Such optimal DCP offers AMs sufficient transition time while compelling a quick shift in their developmental strategies.

Energy-saving improvement of heat integration and heat pump for separating multi-azeotropes mixture via novel pressure swing distillation

The 1-propanol/acetonitrile/isopropyl-alcohol ternary mixture has two pressure-sensitive azeotropes, it can be separated by pressure swing distillation. A novel pressure swing distillation process (NPSD) with the pure ISOPR as self-solvent has been developed which is a perspective on the reduction of the flowrate in the recycle stream. Two triple-columns pressure swing distillation processes (TCPSD) are discussed as the comparative process. NPSD process shows the best economic performance in those processes. Subsequently, the heat integration and heat pump technology are applicated into NPSD process for exploring the further energy save. The NPSD-TVR process which using three compressors shows 63.24%, 68.56%, 69.09% and 62.76% reduction on TAC, TEC, Gas emissions and exergy destruction with the comparison of the TCPSD process.

CPU frequency scheduling of real-time applications on embedded devices with temporal encoding-based deep reinforcement learning

Small devices are frequently used in IoT and smart-city applications to perform periodic dedicated tasks with soft deadlines. This work focuses on developing methods to derive efficient power-management methods for periodic tasks on small devices. We first study the limitations of the existing Linux built-in methods used in small devices. We illustrate three typical workload/system patterns that are challenging to manage with Linux’s built-in solutions. We develop a reinforcement-learning-based technique with temporal encoding to derive an effective DVFS governor even with the presence of the three system patterns. The derived governor uses only one performance counter, the same as the built-in Linux mechanism, and does not require an explicit task model for the workload. We implemented a prototype system on the Nvidia Jetson Nano Board and experimented with it with six applications, including two self-designed and four benchmark applications. Under different deadline constraints, our approach can quickly derive a DVFS governor that can adapt to performance requirements and outperform the built-in Linux approach in energy saving. On Mibench workloads, with performance slack ranging from 0.04 s to 0.4 s, the proposed method can save 3%–11% more energy compared to Ondemand. AudioReg and FaceReg applications tested have 5%–14% energy-saving improvement. We have open-sourced the implementation of our in-kernel quantized neural network engine. The codebase can be found at: https://github.com/coladog/tinyagent.

Analyzing Consultancy on Production Systems Based on the Digital Triplet Concept

This study aims to analyze the process flow of skilled consultants who utilize production improvement know-how and digital technology to enhance production systems in external companies. The concept of a Digital Triplet (D3), which expands the authors’ Digital Twin framework to include the intelligent activity world, is adopted as it aligns with this study’s objective. Given the complexity of the problems faced by production system consulting and the resulting inadequacy of reusing decision-making processes of skilled engineers based on the Generalized Process Model (GPM) using D3, a production system consulting modeling method is proposed. This method incorporates the Generalized Production System Consulting Process Model (GCPM) to generalize the production system consulting process. Using the proposed method, a case study focusing on energy-saving improvements was conducted to describe and analyze the consulting process of skilled consultants. The results show that the proposed method effectively captures the process flow of skilled consultants while considering the iterative structure of the GCPM. Additionally, utilizing the GCPM enables a comprehensive view of the entire process, facilitating an understanding of how knowledge and tools are utilized in various contexts.

A Concise Review of Multicomponent Reactions Using Novel Heterogeneous Catalysts under Microwave Irradiation

Multi-component reactions for the construction of heterocycles have been fascinated by microwave energy as an alternative technique of heating, owing to the advantages over traditional reflux methods. The heterogeneous catalysts contribute significantly towards recycling, harmless, easy filtration, catalyst preparation, more life span, abundance, and product yields. With novel and creative uses in organic and peptide synthesis, polymer chemistry, material sciences, nanotechnology, and biological processes, the usage of microwave energy has rapidly increased during the past 20 years. This article covers multicomponent reactions involving construction of chromenes, pyridines, pyrroles, triazoles, pyrazoles, tetrazoles, trans and cis julolidines using heterogeneous catalysts under microwave. It provides an overview of contemporary microwave-assisted heterogeneous catalytic reactions. Microwave chemistry is now an established technology with several advantages regarding reaction rate and production yield, improving energy savings as confirmed by many applications. Due to the widespread curiosity in medicinal chemistry, the heterogeneously catalysed construction of heterocycles under microwave irradiation is explored to reduce time and energy. By considering various aspects of economy, eco-friendly, and user-friendly factors, this review focuses on recent advances in the multi-component construction of heterocycles using heterogeneous catalysts under microwave irradiation. This review also discusses the benefits and limitations of reaction conditions and yields from the literature reports for the past five years.

Advanced modeling and energy-saving-oriented assessment of control strategies for air-cooled chillers in space cooling applications

Chillers are reference technologies to meet the demand for space cooling in the tertiary and commercial sectors. Meantime, being power-to-cold technologies, they could increase the flexibility of these buildings in those contexts of a high share of electricity from renewable energy sources through new control strategies. To reliably assess the achievable energy savings in these novel applications, models capable of simulating not only the steady-state operation but also the dynamic response are required. However, the operation of these systems is usually evaluated through highly simplified models, also omitting controls. To fill this gap, this paper proposes an integrated thermodynamic and control modeling for an air-cooled chiller, accounting for usual and innovative control strategies. To show the capabilities of the model, an air-cooled chiller serving an office in the Mediterranean area is assumed. Both a variable-speed chiller and a constant-speed chiller with sequential control for compressors are simulated. Results show that for a variable-speed chiller, the set point for the supplied cold water is met, and the thermal inertia of the hydronic loop affects the reaching of the steady-state operation. In the case of a constant-speed chiller with sequential control, the number of “ON-OFF” cycles for each compressor is monitored and the minimum inertia of the hydronic loop for the safe operation of compressors is found. The analysis reveals that a variable temperature setpoint for the supplied water allows for a percentage increase in the energy performance between 10.8% and 60.3%. The proposed model enables the analysis of innovative controls aimed at improving energy savings and increasing building flexibility.

Microclimatic HVAC system for nano painted rooms using PSO based occupancy regression controller

In this paper, PSO-OMRC model is proposed for heating ventilation and air conditioning (HVAC) system. The PSO-OMRC predicts SPT in HVAC for energy saving and thermal comfort of persons in room. PSO-OMRC used data collected from temperature, humidity, PIR, and thermal camera sensors in HVAC installed rooms for SPT prediction. In existing HVAC systems, SPT is constant and consumes more energy. Moreover, SPT prediction using traditional machine learning algorithms considers various parameters such as room temperature, humidity and person count in room. Existing algorithms considers the above parameters for energy saving in HVAC and thermal comfort for persons, whereas other parameters such as room walls and ceiling temperature, human body temperature need to be considered for SPT prediction and for energy saving in HVAC. The PSO-OMRC model predicts the SPT based on number of occupants and microclimatic conditions such as environment temperature, humidity, room wall temperature and ceiling temperature. From experimental results, the utilization of the thermal image occupant model provides significant energy savings in the PSO-OMRC model. In nano coated room, PSO-OMRC model is evaluated with the implementation of a thermal image occupant model to improve energy savings, resulting in approximately 25.2% of energy savings while maintaining the level of thermal comfort.