Total Energy Consumption Research Articles

Heat integrated extractive coupled pressure-swing distillation with dividing wall column technology for separating phenol/pyridine from coal gasification wastewater

The phenol and pyridine of coal gasification wastewater (CGW) are extremely toxic pollutants. Thus, there is an urgent need to develop a new process for the effective treatment of phenol and pyridine in CGW. Vapor-liquid equilibrium (VLE) and COSMO-SAC model analysis were used to select the entrainer, and interaction region indicator analysis was applied to elucidate the separation mechanism. Aiming to minimize total annual cost (TAC), the optimal process parameters were established through a sequential iterative procedure; extractive pressure-swing distillation process: separation sequence E (EPSDP-SE) process exhibiting the best performance. Heat integrated extractive pressure-swing distillation with dividing wall column process: separation sequence E (HI-DWC-EPSDP-SE) was developed by integrating a thermal process and dividing wall distillation technology. In comparison to EPSDP-SE, HI-DWC-EPSDP-SE achieved reductions of 26.97% in TAC, 38.89% in total operation cost, and 6.21% in total capital cost. Additionally, it lowered total acid gas emissions and energy consumption by 39.98% and enhanced thermodynamic efficiency by 54.72%. The HI-DWC-EPSDP-SE process showcases exceptional separation performance in terms of economy, energy consumption, and environmental impact.

Cooperative scheduling of airport ground electric service vehicles considering workload balance: A column generation approach

Under the trend of electrification in civil aviation, airports are promoting electric ground vehicles to reduce carbon emissions. This paper conducts on electric airport ground service vehicles in scheduling optimization problems. Moreover, driver psychology is also a growing concern as it can affect the efficiency and safety of ground services. Workload balance is one of the major factors affecting driver psychology. In the context of facing challenges for ground vehicle scheduling, fully using electric vehicles and guaranteeing the workload balance are major concerns in our study. This paper investigates the electric shuttle buses and towing tractors cooperative scheduling considering workload balance optimization problem (e-STCSWB). We introduce a mixed integer programming formulation for e-STCSWB that considers cooperative relationships, workload balance, energy consumption and time windows to minimize total energy consumption costs. The e-STCSWB is obtained small scale exact solutions using the CPLEX solver. Furthermore, a column generation approach is developed, which is enhanced through a problem-specific strategy. Extensive computational results using real-world instances from one of China’s largest airports show that it can obtain optimal or near-optimal solutions. The problem-specific strategy significantly improve the performance in terms of solution quality and solution time. Finally, we perform sensitivity analyses to demonstrate the impact under different numbers of vehicles and workload balance requirements parameter settings.

Steam Calcination of Magnesite for the Production of Reactive Magnesia

ABSTRACT This study systematically evaluates steam calcination as a method for producing magnesium oxide (MgO) from magnesite (MgCO₃), by testing actual industry samples under varying conditions. While the potential benefits of steam calcination were previously acknowledged, this work provides the first comprehensive quantification of its advantages over conventional methods such as flue gas and flue gas with steam calcination. The results demonstrate that steam calcination significantly lowers the decomposition temperature of MgCO₃, achieving near-complete decomposition within 20 min at 600°C while also accelerating the decomposition of dolomite and calcite present in magnesite ores. Steam calcination enhances the specific surface area of MgO to 49.61 m2/g, optimizes structural properties such as pore volume, and improves reactivity, with reaction times as short as 45 s. Environmentally, steam calcination reduces total energy consumption by approximately 15%, and simplifies CO₂ capture by separating it from condensed steam, enabling a potential carbon emissions reduction of 1.09 kg CO₂-e per kilogram of MgO produced. Furthermore, its compatibility with renewable energy sources aligns with sustainable industrial goals, establishing steam calcination as a highly efficient and environmentally friendly alternative for industrial MgO production.

Energy-exergy and enviro-economic analysis of water-cooled brazed plate building-integrated condenser for cooling appliances in dwellings.

Globally, domestic refrigerators account for over 13% of the total energy consumption in residential buildings. The brazed plate water-cooled condenser (BPWCC) is compact in size and an attractive option to reduce the energy consumption of refrigerators using domestic water tanks. This study evaluated the performance of a household refrigerator with a secondary refrigerant calorimeter and BPWCC, using an appropriate experimental setup. Water storage tanks with capacities of 750l, 1000l, and 2000l supplied cooling water to BPWCC. The study evaluated how well the modified domestic refrigerator (MDR) worked with R134a and a blend of R290 and R600a (54.8:45.2), using 108g and 50g of charge, respectively. When the MDR was charged with the R290/R600a blend, its COP, exergy efficiency, per-day energy consumption, and total equivalent warming impact were 0.93-1.18, 24-24.8%, 1.28-1.4 kWh, and 5019, respectively. BPWCC's exergy efficiency went up by 25-55% when the condensing temperature went down, and it used 11.5-27% less energy per day than air-cooled condensers. Besides reducing the cost rate by 32-37%, the modified system showed a payback period below 3years. However, to mature the proposed system, future research must focus on several issues related to the integration of the domestic water system with the BPWCC.

Historical Analysis of Real Energy Consumption and Indoor Conditions in Single-Family Passive Building

The paper includes a historical analysis of real energy consumption and indoor conditions in a single-family passive building located in Warsaw, Poland. Passive houses have emerged as a sustainable alternative to the conventional construction of houses, having advantages such as low energy consumption, comfortable indoor temperatures, an environmentally friendly nature, and low carbon emissions. This research consists of indoor temperature assessments over a 5-year period (2018–2022) which include comfort assessments made in accordance with the standard EN 16798-1 and precise assessments made for extreme weather events over a two-week critical period including the heating and cooling seasons. The real energy consumption analysis, including electric heating, outdoor lighting, indoor lighting, ventilation, and domestic hot water, was compared against passive house and nearly-zero energy standards. The results of the study show that the building is thermally comfortable to live in, as it remained mainly in the first comfort category, IEQ I. There was no such issue as overheating and underheating even during extreme weather events. The energy need for heating remained very close to the passive standard, namely 15 kWh/(m2·year). The total primary energy consumption for heating, hot water, and electricity meets the standard required value of 120 kWh/(m2·year). These findings demonstrate the effectiveness of passive house design principles at achieving high levels of thermal comfort and energy efficiency in cold climates. In addition, it is demonstrated that it is possible to maintain comfortable indoor temperatures (even with outdoor air temperatures reaching 35 °C) without air conditioning or cooling systems. The integration of a photovoltaic system offers a viable pathway toward transforming the building into a zero-energy standard, contributing to sustainability goals and reducing carbon emissions.

Glycemic control in women with GDM: insights from a randomized controlled pilot trial on plant-based Nordic healthy diet versus moderately carbohydrate restricted diet

BackgroundGestational Diabetes Mellitus (GDM) prevalence is rising worldwide, but optimal dietary strategies remain unclear. The eMOM pilot RCT compared a plant-protein rich Healthy Nordic Diet (HND) and a moderately carbohydrate restricted diet (MCRD) and their potential effects on time in glucose target range (≤ 7.8 mmol/L, %TIR), and on newborn body composition.MethodsForty-two participants were randomized to either HND (n = 20) or MCRD (n = 22) face-to-face nutritional counseling from gestational weeks (GW) 24 + 0—28 + 6 (baseline) until delivery. The HND intervention had no restriction in carbohydrate intake and emphasized plant-based protein sources and Nordic food, while the MCRD had a moderate carbohydrate restriction (~ 40% in proportion to total daily energy consumption, E%). Continuous glucose monitoring was worn for 14 days to assess glucose levels and %TIR. Blood samples for glucose and lipid metabolism and 3-day food diaries were collected at baseline and at GW 34 + 0—35 + 6. Neonatal body composition was measured by air displacement plethysmography. Difference between groups were analysed with t-test and Wilcoxon test.ResultsThirty-two women completed the study. Both groups maintained the %TIR during majority of the time (98.9 and 99.3% for MCRD and HND respectively, p = 0.921) in GW 34 + 0 – 35 + 6. The mean glucose was lower in the MCRD group compared to the HND group (5.0 SD 1.03 vs. 5.2 SD 0.96 mmol/l, p < 0.001). No differences were observed in glucose variability, lipid metabolism, gestational weight gain, or in the body composition of the newborns. HND had lower diet macronutrient adherence than the MCRD, resulting in similar macronutrient composition in both groups. The mean macronutrient intakes were fat: 40.6 vs. 39.5 E%, carbohydrate: 40.5 vs. 42.4 E%, protein: 18.9 vs. 18.1 E% for the MCRD and HND groups, respectively. The HND decreased intake of meat and increased fish consumption significantly compared to the MCRD.ConclusionsBoth a moderately restricted carbohydrate diet and a diet focused on plant-based protein effectively maintained a large time within the treatment target range in women with GDM. Further research could explore the impact of protein quantity and sources in maternal diets on glycemic control and newborn outcomes.Trial registrationThe eMOM pilot trial is registered in Clinicaltrials.gov (21/09/2018, NCT03681054).

Energy efficient green building design utlilising renewable energy and low-carbon development technologies

The core issue these days is to reduce carbon dioxide (CO2) emission and prevent the hole in the ozone layer. Numerous emerging environmental crises successfully tackled by coordinated global efforts in the past few years. Despite this, the present climate emergency is a much more serious threat than anything we have ever encountered and requires much more action consequently. Today, more attention is paid on green buildings in the society, which will make the use of sustainable buildings for the entire life cycle a necessity for future generations. Hence, this paper proposes a novel design model of an energy-efficient residential green building with low carbon emissions to maintain the health and enhances the productivity and living standards of inhabitants. Green building technology is utilized to enhance energy efficiency and lower carbon emissions. This design considers green, recyclable, and eco-friendly building materials, which are beneficial for human health and comply with relevant Indian standards and building codes. This building design proposes renewable energy sources (RES) integrated with the power grid, although RES powers most of the load of the green building. The proposed green building design shows effective results, i.e., building energy consumption has reduced by 50.54%, total energy consumption cost has reduced by 57.41%, and CO2 emission per month has reduced by 50.54%. In addition, stormwater-harvesting system is proposed to collect 54322.23 litres of rainwater annually, which helps in water conservation and contributes to improving the groundwater level. The proposed solid waste management plan has contributed to the achievement of regional and national Sustainable Development Goals (SDGs). Finally, there are some suggestions to promote the use of green buildings for sustainable development.

Energy-Efficient Distributed Welding Shop Scheduling Based on Multi-Objective Seagull Algorithm

For the energy-efficient distributed welding shop scheduling problem, focusing on the scheduling of production in a distributed welding shop, a mathematical model is established with the objective of minimizing the makespan and total energy consumption. In order to solve this optimization problem, an improved multi-objective seagull algorithm (IMOSOA) is proposed. The algorithm introduces three main enhancements: designing a weight matrix based on multiple critical paths to update the number of welders allocated to each job, redesigning the discretization operations of the multi-objective seagull algorithm according to the characteristics of distributed welding shop, and incorporating a Pareto front selection strategy. This strategy uses a new crowding distance calculation to resolve cases where non-dominated solutions at the same dominance level have equal crowding distances, thereby improving the next generation of solutions. These improvements not only reduce the maximum completion time and total energy consumption but also enhance search efficiency. Finally, the IMOSOA is compared with other algorithms under different scale examples, and the results show that the energy consumption is at least 11.7% lower than that of the comparison algorithm, which verifies the superiority of the IMOSOA.

Based on AVL-CRUISE Hybrid and Electric Vehicle Simulation and FTP-72 Circle Power Consumption Factor Analysis

With the rapid advancement of technology and the growing scarcity of energy resources, finding effective ways to reduce power consumption has become increasingly crucial. Hybrid and electric vehicles play a vital role in this endeavor, warranting deeper exploration and focus. In this study, I utilized AVL-Cruise software, a powerful tool for simulation and data modeling, to conduct comprehensive simulations of hybrid and electric vehicle performance, with a specific focus on analyzing the FTP72 cycle.The simulation results are highly encouraging. The vehicle achieved a maximum speed of 164 km/h, an acceleration rate of 3 m/s, a 0 to 100 km/h acceleration time of 9 seconds, and a maximum climbing slope of approximately 74%. These figures not only demonstrate that hybrid and electric vehicles can meet performance expectations but also highlight their capability to deliver on key metrics. Furthermore, the total energy consumption was recorded at 4200 kJ, underscoring the efficiency of these vehicles.In addition to the simulation outcomes, I delved into various factors that influence vehicle performance, including vehicle mass, drag coefficient, and motor power.Using MATLAB, I conducted a detailed analysis of the relationships between these factors and energy consumption, incorporating residual analysis and deriving a mathematical equation to describe these interactions.Through this extensive data analysis and model fitting, we gain a deeper understanding of hybrid and electric vehicle dynamics. This research not only contributes to the field but also has the potential to drive further development, broadening the focus and scope of hybrid and electric vehicle technology

Navigating the Challenges of Sustainability in the Food Processing Chain: Insights into Energy Interventions to Reduce Footprint

This review paper examines the critical intersection of energy consumption and environmental impacts within the global food system, emphasizing the substantial footprint (including land usage, costs, food loss and waste, and carbon and water footprints) associated with current practices. The study delineates the high energy demands and ecological burdens of food production, trade, and consumption through a comprehensive bibliographic analysis of high-impact research papers, authoritative reports, and databases. The paper systematically analyzes and synthesizes data to characterize the food industry’s current energy use patterns and environmental impacts. The results underscore a pressing need for strategic interventions to enhance food system efficiency and reduce the footprint. In light of the projected population growth and increasing food demand, the study advocates for a paradigm shift towards more sustainable and resilient food production practices, adopting energy-efficient technologies, promoting sustainable dietary habits, and strengthening global cooperation among stakeholders to achieve the Sustainable Development Goals. Investigations have revealed that the food system is highly energy-intensive, accounting for approximately 30% of total energy consumption (200 EJ per year). The sector remains heavily reliant on fossil fuels. Associated greenhouse gas (GHG) emissions, which constitute 26% of all anthropogenic emissions, have shown a linear growth trend, reaching 16.6 GtCO2eq in 2015 and projected to approach 18.6 GtCO2eq in the coming years. Notably, 6% of these emissions result from food never consumed. While the water footprint has slightly decreased recently, its demand is expected to increase by 20% to 30%, potentially reaching between 5500 and 6000 km3 annually by 2050. Energy efficiency interventions are estimated to save up to 20%, with a favorable payback period, as evidenced by several practical implementations.

Carbon Peak and Carbon Neutrality Scenarios for Key Industries in Jiangsu Province Based on LEAP Model

In this study, strategies for meeting the carbon peak and carbon neutral targets are developed through an in-depth analysis of future energy demand and carbon emission scenarios in Jiangsu Province. The LEAP model was used to forecast energy demand and carbon emissions under different scenarios from 2021 to 2060. Under the baseline scenario, the energy demand of key industries in Jiangsu Province showed a high rate of growth, with energy consumption increasing by 2.35 times from 2021 to 2060, with an average annual growth rate of 3.46%. In contrast, under the Policy Implementation, Sustainable Development, and Innovation Promotion scenarios, total energy consumption decreased significantly by 45.98%, 58.96%, and 86.13%, respectively, demonstrating varying degrees of energy-saving potential. Projections of carbon emissions showed that the total carbon emissions under the baseline scenario showed a continuous growth trend, reaching 599 million t in 2060, with an average annual growth rate of 4.47%. In contrast, under the Policy Implementation, Sustainable Development, and Innovation Advancement Scenario, carbon emissions were significantly lower, as evidenced by a peak reaching 2025 or 2030 and a gradual decline. The industrial sector decreased year by year, the transportation sector showed an increasing trend, and the construction sector was relatively stable. Overall, with industrial restructuring and technological upgrading, Jiangsu Province is expected to achieve a gradual reduction in carbon emissions, but the increasing trend of carbon emissions from the transportation sector requires further monitoring.

Supervisory controller for minimizing fuel consumption and NOx emissions of plug-in hybrid electric vehicles operating in zero-emission zones

This paper introduces a supervisory controller designed for Plug-in Hybrid Electric Vehicles (PHEVs) to minimize total energy consumption and tailpipe NOx emissions while adhering to Zero Emission Zone (ZEZ) constraints. The case study exploits historical driving cycle data from an urban bus route in Zurich to analyze trip correlations, where a ZEZ restriction was added to assess the vehicle performance under such conditions. A PHEV bus model was built, integrating the powertrain and After-Treatment System (ATS), where an electric heater is included to mitigate NOx emissions. The supervisory controller is tasked with determining the optimal power split and the electric heater power to ensure adherence to feasible operating conditions along the route. Historical driving cycle data analysis demonstrates that the speed profiles along the selected route exhibit similarities. This observation is leveraged by a Dynamic Programming (DP) optimization, where an arbitrary bus trip is employed to generate a cost-to-go matrix. Then, the resulting cos-to-go matrix was indexed by the position in the route and is used on the real-time controller by applying the one-step look-ahead roll-out algorithm. Simulation results demonstrate the controller effectiveness in addressing ZEZ restrictions, presenting a trade-off between energy consumption and tailpipe NOx emissions. A benchmark was carried out, comparing the results obtained with the DP solution as the baseline, assuming perfect knowledge of driving cycle disturbances, revealing a 7% increase in tailpipe NOx emissions and a 1.3% increase in fuel consumption compared to the theoretical minimum and fulfilling the ZEZ restrictions in all the cases.

Private equity renewable energy investments in India.

India is anticipated to grow its total energy consumption and CO2 emissions by more than any other country over the next two decades. India will have to attract around $400 billion in financing to realize its 500GW target of renewable energy by 2030. Given complex renewable energy sector risks, rapidly scaling-up risk-friendly private equity financing will be critical to achieve India's target. This research seeks to answer questions regarding the motivation, perceptions, strategies, and investment behavior of private equity investors in the Indian renewable energy sector. The answers to these questions presented herein have been distilled from primary research interviews with 40 executive-level sector practitioners and literature analysis. This research finds that global-macro forces and sector-specific appeal are attracting varied investors to the sector. These investors primarily deploy capital in existing developer platforms, creating control developer platforms, and Infrastructure Investment Trusts. Critical investment criteria and value creation strategies of these investors are comprehensively discussed. Emerging investment opportunities to create new renewable energy sector value by partnering with companies across the power sector value chain are also presented. This research concludes that despite significant sector risks, investors remain confident they can achieve outsized risk-adjusted returns relative to most other global infrastructure assets. This optimism stems from the confidence of investors in their own ability to manage risk-return dynamics through judicious investment selection and management strategies, the sector's uniquely large demand growth and market size, and the Indian central government's perceived strong commitment to finding creative solutions to chronic sector issues. Lessons from the investment insights, themes and factor analyses discussed herein can be drawn upon in evaluating renewable energy investments and policymaking worldwide.

Investigation of boiler energy consumption in the gas refinery units using RSM ANN and Aspen HYSYS.

In order to lower total energy consumption, this study focuses on optimizing energy use in refinery boilers. Using Aspen HYSYS simulations and modeling approaches like Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM), data from 579 days of boiler operation was gathered and examined. Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) techniques were used in the ANN modeling. Under the same operating circumstances, Aspen HYSYS estimated an energy usage of 1,355m³, whereas the actual consumption was 986m³. While the R2 values for the ANN models were 0.98 for the RBF model and 0.99 for the MLP model, the R2 value derived using RSM was 0.97. Furthermore, the RBF model's performance metrics were 0.0034, whereas the MLP model's were 0.0018. The MLP model is the best choice, according to these findings. It is estimated that burning 26,000m³ of fuel with an air supply of 23m³/h at 25.5°C will result in a steam flow of 525.5 tons per day at 10.5 barg and 256.5°C. According to actual statistics, these circumstances might prevent the release of 27 tons of carbon dioxide by reducing fuel usage by over 10,000m³ per hour. By optimizing the combustion stack's air supply, this decrease is accomplished.

Intelligent Joint Optimization of Deployment and Task Scheduling for Mobile Users in Multi‐UAV‐Assisted MEC System

Mobile edge computing (MEC) servers integrated with multi‐unmanned aerial vehicles (multi‐UAVs) present a new system the multi‐UAV‐assisted MEC system. This system relies on the mobility of the UAVs to reduce the transmission distance between the servers and mobile users, thereby enhancing service quality and minimizing the overall energy consumption. Achieving optimal UAV deployment and precise task scheduling is crucial for improved coverage and service quality in this system. This problem is framed as a nonconvex optimization problem known as joint task scheduling and deployment optimization. Recently, an optimization technique based on a dual‐layer framework: Upper layer optimization and lower layer optimization have been proposed to tackle this problem and achieved superior performance compared to the alternative methods. In this framework, the lower layer was responsible for task scheduling optimization, while the upper layer was designed to assist in optimizing UAV deployment and thus achieving improved coverage and enhanced task scheduling for mobile users, thereby minimizing the total energy consumption. However, further refinement of upper layer optimization is needed to improve the deployment process. In this study, the upper layer undergoes enhancement through key modifications: First, random selection of the solutions is replaced with sequential selection to maintain the unique characteristics of each individual throughout the optimization process, fostering both exploration and exploitation. Second, a selection of recently reported metaheuristic algorithms, such as spider wasp optimizer (SWO), generalized normal distribution optimization (GNDO), and gradient‐based optimizer (GBO), are adapted to optimize UAV deployments. Both improved upper layer and lower layer optimization led to the development of novel, more effective optimization approaches, including IToGBOTaS, IToGNDOTaS, and IToSWOTaS. These techniques are evaluated using nine instances with a variety of mobile tasks ranging from 100 to 900 to test their stability and then compared to different optimization techniques to measure their effectiveness. This comparison is based on several statistical information to determine the superiority and difference between their outcomes. The results reveal that IToGBOTaS and IToSWOTaS exhibit slightly superior performance compared to all other algorithms, showcasing their competitiveness and efficacy in addressing the optimization challenges of the multi‐UAV‐assisted MEC system.

Study on energy-saving design of renewable energy applied to high-rise residential buildings

ABSTRACT Improving building energy systems is a major research hotspot due to the rising demand for indoor comfort and buildings’ increasing energy consumption. The research object in this work is a high-rise residential building in Nanjing. The photovoltaic system and ground source heat pump system are introduced into the traditional cooling and heating source system for energy-saving design of the building. Based on OpenStudio software, two photovoltaic systems, household photovoltaic panels and centralized rooftop photovoltaic panels, are analyzed in terms of dynamic energy efficiency on a time-by-time and day-by-day basis. In addition, the ground source heat pump system is studied in comparison with conventional split air conditioning system. Meanwhile, the energy-saving performance of systems is analyzed during the cooling, heating and transition seasons. The results showed that both PV systems can afford a total of 39.5% of the electricity demand for lighting and equipment systems throughout the year. The total primary energy consumption per unit area of the optimized building model is 51.17 kWh/(m2·a), which is 35.2% lower than that of the original building model. In addition, the energy replacement rate of the photovoltaic system is 23.7% and the contribution of renewable energy from ground source heat pump is 38.76%, which meets the national standard for near-zero energy buildings.

The Impact of Economic and Environmental Factors on the Consumption of Renewable Energy: The Case of Kazakhstan

The article examines the impact of economic and environmental factors on the consumption of renewable energy sources (RES) in Kazakhstan. The study is based on a quantitative analysis of data, including adjusted net savings (ANS), the share of renewable electricity output (REO), and carbon dioxide (CO₂) emissions per capita. The dependent variable in this analysis is the share of renewable energy in the total final energy consumption. The results of the regression analysis revealed the following relationships: an increase in the share of electricity production from renewable energy sources (REO) by 1% leads to an increase in their consumption by 0.119% (p &lt; 0.01), which is associated with reduced production costs and improved accessibility. Adjusted net savings (ANS) also have a positive effect: their growth by 1% increases renewable energy consumption by 0.055% (p &lt; 0.05), confirming the role of economic stability and investment in the development of green energy. At the same time, an increase in CO₂ emissions negatively affects renewable energy consumption, decreasing it by 0.154% for each additional ton of CO₂ per capita (p &lt; 0.01), which is explained by the predominance of traditional hydrocarbon energy sources. The study highlights that the sustainable development of renewable energy sources requires a comprehensive approach, including the stimulation of its production, reduction of carbon emissions, and maintenance of economic stability. The results have practical value for the formation of Kazakhstan's state policy aimed at transition to sustainable energy and reducing environmental pressure.

A reinforcement learning based memetic algorithm for energy-efficient distributed two-stage flexible job shop scheduling problem

Given the increasingly severe environmental challenges, distributed green manufacturing has garnered significant academic and industrial interest. This paper addresses the distributed two-stage flexible job shop scheduling problem (DTFJSP) under time-of-use (TOU) electricity pricing, with the objective of minimizing both makespan and total energy consumption costs (TEC). To tackle the problem, a hybrid memetic algorithm (HMA) is proposed. Initially, a three-tier vector encoding scheme and three population initialization strategies are devised. Subsequently, global search operators are tailored to the problem’s characteristics, and seven local search algorithms based on Q-learning are introduced. Additionally, an energy-saving operator is incorporated. Finally, orthogonal experimental design is employed to set algorithm parameters and validate the efficacy of some components. The numerical experimental results demonstrate that the proposed local search operator and energy-saving strategy are effective. Furthermore, the HMA exhibits superior diversity, breadth, and distribution compared to VNS, CMA, and NSGA-II, thereby validating the efficacy of the specialized designs of the HMA in addressing the DTFJSP.

Enhancing scalable reconfigurable manufacturing systems through robust optimisation: energy efficiency and cost minimisation under uncertainty

Reconfigurable manufacturing systems are dynamic systems designed with scalable and flexible production capabilities to address changing market demands. This paper presents a novel multi-objective integer programming model aimed at optimising the configuration and capacity scalability of reconfigurable machine tools in uncertain environments. The model focuses on minimising three key objectives: total energy consumption, unused capacity, and total cost. It incorporates critical manufacturing constraints such as peak power thresholds and limited tool availability. To effectively manage uncertainty, particularly in demand fluctuations, a scenario-based robust optimisation approach is applied, striking a balance between solution robustness and model adaptability. A comprehensive case study demonstrates the model's effectiveness, comparing deterministic and uncertain solutions. Additionally, sensitivity analyses are performed on parameters such as peak power thresholds, risk coefficients, and infeasibility weights, highlighting their impact on system performance. The results provide insights into the efficient design and operation of scalable reconfigurable manufacturing systems under uncertainty, with recommendations for future research directions.

APPLICATION OF DELIGHTING TO OPTIMIZE WINDOW-TO-WALL RATIO (WWR) IN BUILDINGS IN INDIAN CLIMATIC CONDITIONS

This research focuses on the energy performance of buildings with glass windows to allow daylight in three different locations in India. The utilization of glass in modern commercial buildings is rapidly increasing for aesthetic views and daylighting through the glazing. Much research has been conducted on WWR and daylight integration into the building through window glazing. In this study, the window area is distributed to different cardinal directions and the result is compared with the reference building in the study location. Focus has been given to optimizing WWR for different window combinations. Energy consumption in eleven different window combinations and five different WWRs were studied. The result shows the reduction of total energy consumption of buildings with different window combinations as a result of the integration of daylight. Window combinations for higher WWR and the optimum value of WWR in all window combinations are determined.