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The purpose of this work is to explore the decrease of total used power in cell‐free millimetre‐wave (mm‐Wave) massive multiple‐input multiple‐output (MIMO) systems, which can be regarded an essential technology for future wireless generations to improve system performance. One of the most important strategies for reducing total power consumption is to activate and deactivate radio frequency (RF) chains at each access point (AP) in the coverage region. Nonetheless, the optimization issue for this methodology is NP‐hard, and an exhaustive search method may be used to determine the ideal number of RF chains at each AP in the cell‐free network. Unfortunately, the exhaustive searching approach is prohibitively complicated, indicating that it is unworkable when there are a significant number of APs in the service region. Furthermore, present RF chain selection approaches prioritize decreasing consumed power and complexity at the price of system performance in terms of total possible rate. This research solves this issue by introducing a unique RF chain selection approach that combines Hungarian and genetic algorithms. The fact that the genetic algorithm (GA) can readily determine the ideal number of active RF chains, yet this technique generally entails significant complexity in large‐scale cell‐free networks, prompted this notion. As a result, the Hungarian method is used early in the GA to overcome the complexity problem while still retaining system performance. In addition, the suggested system employs a semi‐centralized hybrid beamforming architecture in which all analogue combiners for all APs are operated at a central processing unit using channel state information. In addition, each AP has a fully linked phase shifters network and restricted RF chains connecting to its antennas. Finally, simulation findings reveal that, when compared to state‐of‐the‐art techniques, the suggested approach achieves the maximum attainable rate and overall energy efficiency with a tolerable computational complexity. [ABSTRACT FROM AUTHOR]

3D integration is one of the scalable multiprocessor design solutions. The main challenge in 3D design is temperature traps, especially in the upper layers and hot spots. The current studies present solutions for solving temperature and hotspot problems in topology, routing, and mapping levels. Accordingly, the characteristics of temperature-aware core mapping are beneficial for dealing with the challenges of 3D chips. In this paper, the core temperature is modeled based on processing, communication power, neighbor node temperature, distance from the heat sink, and hotspot node. To find the optimal solution, this paper proposes thermal-aware application mapping techniques using genetic algorithms and fuzzy logic to minimize peak temperatures in 3D network-on-chip (NoC) architectures. Fitness functions include the above parameters with the same weight (GSM) and the fitness function with fuzzy logic by considering the effect of distance from heat sink (GFM1) and heat sink distance and the number of neighbor's core (GFM2). Specifically, the fuzzy logic-based GFM1 algorithm demonstrates superior performance over the genetic algorithm-driven GSM approach in reducing power consumption and energy costs associated with inter-core communication traffic. Across the different traffic patterns tested, GFM1 consistently achieves lower energy expenditure, with reductions upward of 50% compared to GSM. This underlines the strengths of the fuzzy technique in enabling thermal-aware mapping to minimize chip temperatures in the presence of intensive workloads and communication. In essence, the key takeaway is that GFM1 outperforms GSM in the context of power and energy metrics linked to handling on-chip traffic between processing cores. [ABSTRACT FROM AUTHOR]

Although the EVRP (Electric Vehicle routing problem) has promising results on the environmental scale, its implementation has proved challenging. The difficulty of the EVRP resides in the limited driving range of the electric vehicles, combined with the significant charging time. While the charging cost is less than the cost of fuel, this charge time adds to the overall travel time and may overlap with customers' time windows. All these factors increased the computational time exponentially and resulted in the need to overlook some constraints such as traffic congestion, road conditions, weather impact on energy consumption, and driving style, to name a few, in order to speed up execution time. While this method proved effective in accelerating the process of the EVRP, it did, however, render the approach unrealistic, as it steered far from real-life settings and made the approach unpredictable when facing dynamic and changing parameters. In this paper, we try to remedy this issue by proposing an approach in which we try to replicate real-life parameters such as heterogenous fleets, energy consumption, and infrastructure data. The objective of our approach was to minimize the total travel time, travel distance, energy consumed, and the number of vehicles deployed. To solve this problem, we propose a three-stages approach, in which the first stage consists of a newly developed dispatching approach where customers are assigned to vehicles. The second stage uses the genetic algorithm to find a set of optimal paths, and, finally, in the third stage, charging stations are inserted in the selected paths. Upon testing our approach on Solomon's instances, our approach proved effective in finding optimal solutions in a reasonable time for five- to fifteen-customer datasets. However, when trying to solve larger datasets, the approach was slowed down by the extreme number of constraints it had to satisfy. [ABSTRACT FROM AUTHOR]

Given the increasing severity of energy shortages, the exploration of effective strategies to optimize energy structures has become imperative. This requires careful consideration of energy consumption patterns, especially since these data are fundamental inputs for policy formulation. Given the uncertainty in the rate of change in energy consumption, this paper proposes an optimized multivariable grey Bernoulli model that is rooted in the grey Bernoulli model and incorporates background values and genetic algorithms. The grey Bernoulli model effectively linearizes nonlinear problems, thus simplifying computational procedures. In addition, to account for random fluctuations of relevant factors that may affect the model's predictions, this model introduces nonlinear correction terms that allow simulation and prediction values to adhere to the grey index law. The incorporation of background values enhances the model's ability to process information, providing it with a superior grasp of real data. Genetic algorithms can be used to refine the model's parameters, increasing its adaptability and precision. Finally, this paper applies the refined model to examine China's energy consumption patterns, validating its efficacy and versatility. Furthermore, energy consumption patterns for the next four years are forecast, with the analysis revealing that the growth rate of energy consumption from 2021 to 2024 shows a downward trend, particularly notable in 2024, where the growth rate is 1.64%. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

This paper aims to study the feasibility of municipal sewage sludge utilization as an energy source in a polygeneration system. This system offers distinctive benefits such as contribution to the principled removal of sewage sludge, simultaneous utilization of raw and digested sludge in different parts of the system, and production of renewable hydrogen from bio-waste. 4E (energy, exergy, exergoeconomic, and environmental) analyses, are performed to understand the system performance comprehensively. Then, parametric studies are examined the impact of changing the values of main parameters on the system operation. Afterward, a multi-objective optimization based on a genetic algorithm is carried out to achieve optimal values, considering a trade-off between the exergy efficiency and the total cost rate. Meanwhile, this work harnesses the potential of artificial neural networks to expedite complex and time-consuming optimization processes. According to the results, the gasifier exhibits the highest rate of exergy destruction, and the primary cost of consumption is attributed to its heat supply. The multi-objective optimization findings show that the optimum point has an exergy efficiency of 38.26 % and a total cost rate of 58.17 M$/year. The hydrogen production rate, energy efficiency, and net power generation rate for the optimal case are determined as 1692 kg/h, 35.24 %, and 4269 kW, respectively. Also, the unit cost of hydrogen in the optimal case is obtained 1.49 $/kg which offers a cost-effective solution for hydrogen production. [Display omitted] • 4E analysis and optimization performed on a bio-waste driven polygeneration system. • Plant optimized using artificial neural network and multi-objective genetic algorithm. • The feasibility of integrating a membrane for hydrogen separation investigated. • The optimum exergy efficiency and total cost rate obtained 38.26 % and 58.17 M$/year. • Hydrogen and electricity cost of 1.49 $/kg and 0.0145 $/kWh achieved for optimal case. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach for optimal charge scheduling and on-board vehicular control of electrified fleets based on synthetic driving cycles. The proposed approach is conducted within a real case-study in Cairo, Egypt, whereto a representative distance-based driving cycle has been synthesized using K-means clustering over a sliding horizon of gathered data-sets. Two multi-objective problems defining optimal charge scheduling and vehicular control have been formulated to achieve minimal energy consumption and operating cost of the fleet. Non-dominant genetic algorithm (NSGA-II) has been implemented to solve the optimization problems jointly considering fluctuating electricity cost of the grid. The comparative evaluation of results reveals an improvement of 19% and 28% in energy consumption and retention of on-board energy accordingly, with less than 2% mitigation of driveability. Moreover, a reduction of 40.8%, 20%, and 21.9% in fleet size, required charging stations, and annual recharging cost respectively has been realized. The main innovation of this work can be put forward as the ability to address the above-mentioned quadrilateral objectives of electrified fleets in a single comprehensive approach, considering synthetic driving cycles and electricity prices to yield a customized-optimal solution. [ABSTRACT FROM AUTHOR]

Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm's efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm's superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems. [ABSTRACT FROM AUTHOR]

In pursuit of the "Dual Carbon Goals" and to mitigate the adverse effects of "power supply restrictions," a microgrid scheme integrating wind and solar power with hydrogen energy storage is proposed. This paper introduces the principles of system capacity configuration and establishes a mathematical model. This research offers a novel method for configuring wind and solar hydrogen storage systems called quantum-enhanced multi-objective collaboration. This work intends to address the complicated issues of achieving effective energy storage, reducing prices, and maximising renewable energy utilisation by leveraging the capabilities of quantum computing. A multi-objective capacity optimization configuration model for wind–solar–hydrogen energy storage is developed using Homer Pro software and an enhanced BAS-GA algorithm. Under off-grid operating conditions, the wind–solar–hydrogen energy storage system's capacity optimization configuration model is validated through practical examples. The results indicate that, in comparison to wind storage, solar storage, wind–solar storage, and wind–solar–diesel storage systems, the net present value and levelized cost of electricity of the wind–solar–hydrogen energy storage system decrease to 14.25 million RMB and 1.529 RMB/(kW h), respectively. The renewable energy utilization rate increases to 98.7%, and the load shedding rate decreases to 5.50%. [ABSTRACT FROM AUTHOR]

The Flexible Job Shop Scheduling Problem (FJSP) is an extension of the classical Job Shop Scheduling Problem (JSP). The research objective of the traditional FJSP mainly considers the completion time, but ignores the energy consumption of the manufacturing system. In this paper, a mathematical model of the energy-efficient flexible job shop scheduling problem is constructed. The optimization objectives are completion time, delay time, and total equipment energy consumption. To solve the model, an improved non-dominated sorting genetic algorithm (CT-NSGA-II) is proposed to obtain the optimal scheduling solution. First, the heuristic rules of GLR were used to generate the initial population with good quality and diversity. Second, different crossover and variation operators are designed for the process sequencing and equipment selection parts to enhance the diversity of the evolutionary population. The sparsity theory is introduced to find sparse solutions and three neighborhood structures are designed to perform local search on sparse solutions to improve the uniformity of the optimal solution set distribution. Finally, a competitive selection strategy based on the bidding mechanism is proposed for the Pareto optimal solution set to obtain a better scheduling scheme. The experimental results show that the proposed improved algorithm is feasible and effective in the FJSP problem considering energy consumption, and the algorithm has some application value in improving the efficiency of smart shop operation. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) have emerged as a trendy technology for reducing over-dependence on fossil fuels and a global concern of gas emissions across transportation networks. This research aims to design the hybridized drivetrain of a Volkswagen (VW) Jetta MK5 vehicle on the basis of its mathematical background description and a computer-aided simulation (MATLAB/Simulink/Simscape, MATLAB R2023b). The conventional car operates through a five-speed manual gearbox, and a 2.0 TDI internal combustion engine (ICE) is first assessed. A comparative study evaluates the optimal fuel economy between the conventional and the hybrid versions based on a proportional-integral-derivative (PID) controller, whose optimal set-point is predicted and computed by a genetic algorithm (GA). For realistic hybridization, this research integrated a Parker electric motor and the diesel engine of a VW Crafter hybrid vehicle from the faculty of engineering to reduce fuel consumption and optimize the system performance of the proposed car. Moreover, a VCDS measurement unit is developed to collect vehicle data based on real-world driving scenarios. The simulation results are compared with experimental data to validate the model's accuracy. The simulation results prove the effectiveness of the proposed energy management strategy (EMS), with an approximately 89.46% reduction in fuel consumption for the hybrid powertrain compared to the gas-powered traditional vehicle, and 90.05% energy efficiency is achieved. [ABSTRACT FROM AUTHOR]

An essential concern in the modern period is the growing energy consumption in the Internet of Things (IoT) networks, particularly when connecting resource-constrained devices. Prolonging the network's lifespan under these circumstances poses a significant challenge. Clustering is a prominent strategy to enhance the network's operational duration. However, poor cluster heads (CH) selection rapidly drains energy from network nodes. In multi-hop clustering systems, CH consumes more power by forwarding the data from other CHs and its data, creating "hot spots" in the network. We propose a novel evolutionary multi-objective clustering protocol (EMCP) for heterogeneous IoT networks to prolong the network lifetime. In the initial stage, the genetic algorithm optimizes the CH selection process by considering the residual energy, communication radius, the distance between the edge node and CH, and the distance between CHs as fitness parameters. The distance between IoT nodes and CHs determines the clustering process. The energy monitoring module also keeps track of the CHs' power consumption following each data transmission. If the CH's remaining power drops to a threshold value, the new cluster head is one of the IoT nodes close to the previous cluster head with the highest residual energy. In addition, the simulation is performed with multiple edge servers to solve the hot spot problem. The suggested protocol's performance is compared against state-of-the-art protocols for various parameters, including network stability, network longevity, the number of alive nodes, residual energy, and throughput. According to the simulation findings, the EMCP protocol achieves 15,347, 19,493, 24,187, 41,833 rounds, and 1,901,246 packets, respectively, for metrics such as network stability, half nodes dead, three-quarter node dead, last node dead, and throughput. Similarly, EMCP-MS has 17,565, 25,585, 30,410, 45,567 rounds, and 1,957,942 packets for the same metrics. Thus, the EMCP protocol has 94.55%, 80.54%, and 26.15% more life than the GAOC, DDMPEAwithANUM, and OptGACHE protocols. EMCP-MS has 75.49%, 56.55%, and 26.51% more longevity than multi-sink versions of the above-mentioned state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

The energy efficiency optimization of Florida's residential buildings is essential for the reduction of fossil fuel consumption and greenhouse gas emissions. This optimization requires designers to accurately calculate the building energy loads at the design stage to efficiently design the cooling and heating systems. However, due to time and cost constraints, designers usually explore only a few design alternatives and simulate their energy use. This study developed an artificial-neural-network (ANN)-based generative design (GD) framework to automate the design process of detached residences while optimizing their energy performance. The ANN model was developed using the machine learning platform TensorFlow and some Python-based Keras libraries based on a big data set of about 17,000 newly constructed detached residences in Florida between the years 2009 and 2021. The GD framework was established using Autodesk Dynamo and the Autodesk Revit GD add-on that uses the nondominated sorting genetic algorithm (NSGA-II), in which multiple design parameters mainly relating to the house geometry and its energy performance were incorporated. Considering 10 independent variables, including the total wall area, roof area, floor area, and the windows' U-value, the ANN model predicted the required capacities of the cooling and heating systems in detached houses, with R2 values of 0.955 and 0.904, respectively. The ANN then was integrated within the GD framework for performance evaluation purposes. The findings of this study resulted in a fully automated 3-min design process of an energy-efficient detached house envelope, maximizing the productivity of designers and developers and greatly reducing the financial strain and time consumed using traditional techniques. [ABSTRACT FROM AUTHOR]

In this paper, a user thermal comfort criterion based on predicted mean vote (PMV) values is introduced to realize the optimal operation of an improved energy hub (IEH) while considering thermal inertia and user thermal behavior. A three-layer optimization model based on user thermal comfort is constructed which fully considers user thermal comfort demand, IEH operating costs, and energy network constraints. Moreover, since IEH optimization considering user thermal comfort is a multi-objective bilevel optimization (MNBO) problem, this paper proposes an improved multilayer nested quantum genetic algorithm (IMNQGA) to solve it. Finally, the effectiveness of the proposed optimization model and algorithm is verified through the analysis of the four modes. The examples show that the proposed optimal control method can reduce the system's operating costs and improve energy efficiency while satisfying user thermal comfort demand. [ABSTRACT FROM AUTHOR]

In planning the path for a puddling and leveling tractor in a paddy field, achieving multiple leveling across the whole area with minimal traversal while synchronously utilizing a rotary cultivator and a paddy-drive harrow is a key design requirement. This study describes the development of an autonomous puddling and leveling tractor equipped with an optimal coverage path planner (CPP) designed to enable the tractor to level the entire field more than twice using the paddy-drive harrow with foldable wings after prior tilling by the rotary cultivator in fields with irregular shapes. The genetic algorithm was employed to determine the optimal sequence of inner tracks by minimizing the headland turning distance and traversal throughout the field. The feasibility of the developed planner was investigated through simulation studies involving three different shapes of paddy fields, followed by a field test comparing autonomous and manually driven tractors. The simulation results confirmed that the developed CPP effectively achieved the proposed implement strategy and significantly improved efficiency by reducing headland turning distances by 28.5% (pentagon), 33.9% (trapezoid) and 45.0% (rectangle) compared to those obtained from the CPP with gathering track pattern used in a previous study. In the field test, the autonomous tractor demonstrated superior performance in distance and fuel efficiency over the manually operated tractor with comparable leveling capability. Although the operation time was increased by 30.3% because of the lower speed during headland turning, the autonomous tractor reduced travel distance and fuel consumption by 36.1% and 4.7%, respectively, reducing multiple or non-worked areas while resulting in similar altitude variability of 0.24 m (autonomous) compared to 0.19 m (manual). [ABSTRACT FROM AUTHOR]

Cryogenic supercritical hydrogen storage technology is a high-density physical hydrogen storage method, which is a highly promising hydrogen storage approach. However, existing research has mainly focused on the development of storage devices for cryogenic supercritical hydrogen, with limited research on the cooling process of supercritical hydrogen. Therefore, a novel cooling process for cryogenic supercritical hydrogen production based on helium expansion cycle with liquid nitrogen (LN 2) pre-cooling is proposed in this paper. In this process, after being compressed and cooled in multiple stages, the feed hydrogen is pre-cooled by LN 2. Then, cryogenic supercritical hydrogen (10 MPa, −235.15 °C, 63.36 g/L) is obtained by helium multi-stage expansion refrigeration. The process is simulated by Aspen HYSYS and optimized by genetic algorithm (GA) to obtain lower specific energy consumption (SEC). Thermodynamic analyses are conducted to evaluate the process. And compared with the conventional hydrogen liquefaction process, the results show that the proposed process is reasonable and superior. The SEC 0 , figure of merit (FOM), and the total exergy loss of the system are 5.432 kWh/kg H2 , 43.88%, and 289.69 kW respectively. This study indicates that the proposed process has significant implications for the hydrogen industry and provides valuable information for cryogenic supercritical hydrogen storage systems. • A novel high-density cryogenic supercritical hydrogen storage system is proposed. • Twelve key variables in the process are optimized to reduce the energy consumption. • The process has higher exergy efficiency than conventional LH 2 plants. • The volume storage density of hydrogen can reach 63.36 kg/m3 in the system. [ABSTRACT FROM AUTHOR]

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution. [ABSTRACT FROM AUTHOR]

Position-controlled systems driving repetitive tasks are of significant importance in industrial machinery. The electric actuators used in these systems are responsible for a large part of the global energy consumption, indicating that major savings can be made in this field. In this context, motion profile optimization is a very cost-effective solution as it allows for more energy-efficient machines without additional hardware investments or adaptions. In particular, mono-actuated mechanisms with position-dependent system properties have received considerable attention in literature. However, the current state-of-the-art methods often use unbounded design parameters to describe the motion profile. This both increases the computational complexity and hampers the search for a global optimum. In this paper, Chebyshev polynomials are used to describe the motion profile. Moreover, the exact bounds on the Chebyshev design parameters are derived. This both seriously reduces the computational complexity and limits the design space, allowing the application of a global optimizer such as the genetic algorithm. Experiments validate the added value of the chosen approach. In this study, it is found that the energy consumption can be reduced by 62.9% compared to a standard reference motion profile. [ABSTRACT FROM AUTHOR]

Robot comprehensive performance optimization is a high dimensional non-convex problem, which is of great significance in the practice of robotic intelligent manufacturing. Existing methods still face many challenges in performance evaluation, local optima, and planning efficiency. In this paper, a novel method based on accelerated gradient descent and directed-mutagenesis genetic algorithm (AGD-DGA) is proposed for efficient robotic motion planning and comprehensive performance optimization. Firstly, the time, energy consumption, and impact (T-E-I) are taken as the optimization objectives. In particular, the power loss sub-model is constructed, which is rarely considered in the previous research on motion optimization, and it is combined with the driving power sub-model to improve the evaluation accuracy. Next, a novel directed-mutagenesis genetic algorithm (DGA) is proposed to address the local optima problem. Compared with existing methods based on random sampling, the DGA further improves the success rate and efficiency by updating the probability model based on the adaptive Gaussian process. Moreover, a hierarchical weight updating mechanism is established to overcome the impact of additional performance indicators on the success rate of collision-free path planning. To improve planning efficiency, an incremental optimization mechanism and an adaptive fine-tuning strategy for population size are proposed to reduce unnecessary sampling, evaluation, and sorting. The simulation and experimental results in different scenarios demonstrate that the proposed AGD-DGA achieves better results than most of the mainstream algorithms in terms of optimization efficiency, success rate, and motion performance. In addition, it is also verified that the modified energy consumption model can achieve an average evaluation accuracy of more than 90% while maintaining the calculation efficiency. [ABSTRACT FROM AUTHOR]

• The gradient distributed Tesla cold plate is proposed. • Hydro-thermal performance is optimized using kinging model and NSGA-II. • Mass flow rate poses a most significance on energy efficiency. • A maximum reduction of 75.7% for pressure drop is obtained. • Optimal case achieves module maximum temperature difference of 3.6 °C. To address energy-efficient cooling of battery module at a high discharge rate, this work presents a novel gradient distributed Tesla cold plate. Multi-objective optimization is performed to achieve optimal thermal performance and energy efficiency using coupled battery-cold plate simulations, kriging surrogate model and second non-dominated sorting genetic algorithm. Both cold plate structure and operating parameters are considered with objectives of maximum temperature, temperature uniformity and pressure drop. The results demonstrate that the mass flow rate poses a most significance on hydro-thermal performance, followed by channel depth and Tesla valve distance. A conflicting demand for the mass flow rate and channel depth is observed to achieve best hydro-thermal performance. In accordance with Pareto frontier solution, the improvement of pressure drop is most significant with a maximum reduction of 75.7% compared to base case. A moderate mass flow rate with increasing channel depth is recommended as the optimal strategy, yielding moderate liquid convection percentage (51.8%), cooling efficiency (2 3 3) and the highest Nu (6.7). Moreover, present gradient distribution of fractal inlet and Tesla unit contributes to high heat transfer coefficient zones along flow direction, achieving favorable temperature uniformity with maximum temperature difference of module middle section lower than 4 °C. [ABSTRACT FROM AUTHOR]

The computation offloading problem in mobile edge computing (MEC) has received a lot of attention, but service caching is also a research topic that cannot be ignored in MEC. Due to the limited resources available on the Edge Server (ES), a wise computation offloading and service caching policy must be formulated in order to maximize system offload efficiency. In this paper, a many-objective joint optimization computation offloading and service caching model (MaJOCOSC) is designed. The model takes into account the limited computing and storage resources of ES, the delay and energy consumption constraints of different types of tasks, and multiple processing modes of user tasks, and sets delay, energy consumption, task hit service rate, service cache balancing, and load balancing as the five optimization objectives of MaJOCOSC. Meanwhile, a non-dominated sorting genetic algorithm (NSGAIII-ASF&WD) based on achievement scalar function (ASF) and the k-nearest neighbor weighted distance mating selection strategy is proposed for better solving the model. The ASF ensures that the given strategy performs well for each objective value, and the k-nearest neighbor weighted distance provides the user with a diversity of strategies. Simulation results show that NSGAIII-ASF&WD can obtain better objective values when solving the model compared with other many-objective evolutionary algorithms, and a suitable computation offloading and service caching strategy is obtained. [ABSTRACT FROM AUTHOR]

This paper explores the influence of different turbocharging modes and multi-parameter coordinated control on the performance of compression-ignition (CI) aviation piston engine (APE). Firstly, based on a constructed one-dimensional thermodynamic model of a CI APE, the study investigates the effects of different turbocharging modes and combinations of high and low-pressure stage variable geometry turbines (VGT) on the operational performance of the engine. Subsequently, a novel stepwise approximate multi-objective optimization algorithm is proposed, combining backpropagation neural networks and the non-dominated sorting genetic algorithm II. This algorithm evaluates the influence of multiple control parameters on a two-stage turbocharged engine's performance, achieving a balance between fuel economy and reliability. The research shows that equipping CI APEs with twin VGT for supercharging can notably enhance engine performance, enabling the achievement of favorable power recovery objectives at an altitude of 8000 m. The proposed optimization algorithm exhibits strong predictability and reliability, substantially accelerating the computation speed and reducing the data volume by approximately 95%. At an engine speed of 3887 rpm, compared to the unoptimized conditions, the brake specific fuel consumption of the best scenario at altitudes of 2000 m, 4000 m, 6000 m, and 8000 m is reduced by 7.1%, 7.2%, 8.6%, and 6.9%, respectively. • Influences of different turbocharging modes on the performance of a CI APE are investigated. • A novel stepwise approximate multi-objective optimization method based on a hybrid model is proposed. • Explored the altitude-varying control strategy under coordinated multi-parameter optimization. • Increased the altitude ceiling for the CI APE, reaching 82% power recovery target at 8000 m. [ABSTRACT FROM AUTHOR]

The automated berthing of vessels constitutes a high-risk operation. Equipping ships with azimuth and lateral thrusters enhances manoeuvrability, although it poses challenges in achieving optimal thrust allocation (TA) efficiency. This study introduces an improved adaptive non-dominated sorting genetic algorithm III (ANSGAIII) specifically designed to address the TA problem in autonomous ship berthing. The algorithm enhances the objective function by accounting for the nonlinear relationship between actual power consumption and thruster speed, along with considering the treatment of forbidden sectors while maintaining a high level of fidelity. Additionally, the algorithm considers equality constraints, actuator rate and position constraints, and other operational constraints. A modification to the non-dominated sorting method is implemented to improve the processing speed of the optimization algorithm. Significantly, this algorithm surpasses NSGAII, NSGAIII, and CMOEA\D in meeting ship safety and stability requirements while concurrently reducing energy consumption. • An ANSGAIII TA scheme is proposed to enhance berthing safety and energy efficiency. • Cost function and forbidden sector strategy are improved. • The non-dominated sorting method is refined to expedite the TA process. [ABSTRACT FROM AUTHOR]

Given the increasing integration of building photovoltaics and the accelerated transformation of power systems, the supply-demand imbalance in photovoltaic communities has become a pressing issue. This study presents a novel optimization model for collaborative planning and scheduling. The alternating direction multiplier method is utilized to derive an optimal economic scheduling strategy for the community cooperative alliance, while ensuring private information protection. Additionally, the genetic algorithms are employed to ascertain the optimal configuration. The optimization model prioritizes the common good over individual interests in community cooperation alliances, meaning some members may need to compromise their self-interest to attain optimized overall benefits. Therefore, a compensation mechanism based on the Nash negotiation principle is established to ensure fair distribution of benefits. The simulation results demonstrate that the primary energy saving rate, annual cost saving rate, and carbon dioxide emission reduction rate of the photovoltaic community alliance are 42.91%, 32.50%, and 41.81%, respectively, and the self-consumption level of photovoltaic is vastly improved to 98.83% on a typical winter day. The sensitivity analysis illustrates that the photovoltaic panels in buildings contribute to a decrease in energy consumption and carbon emission by 25.57% and 26.15%, respectively. In contrast, they result in a cost increase of 11.05%. [ABSTRACT FROM AUTHOR]

Minimising energy consumption in today's industrial sector is a crucial objective for achieving established climate goals. One effective strategy to enhance efficiency is optimising energy system operations within industries. The initial step in establishing operational optimisation involves developing a comprehensive model of the energy system. This model necessitates a specific structure to meet optimisation requirements. However, creating a model from scratch incurs substantial effort. While numerous companies possess energy models, they often lack the requisite structure for optimisation. Consequently, simplifying existing models can significantly reduce the effort needed to implement operational optimisation. This paper investigates the simplification of intricate industrial energy system models for optimisation purposes. The subsequent sections analyse two distinct approaches. One approach involves linearisation, while the other utilises neural networks. To facilitate a comparative analysis of these approaches, a reference model is developed. The assessment of these methodologies includes an investigation into optimisation robustness, computation time, accuracy concerning the reference model, and the effort required for developing and maintaining the simplified models. It proved that both approaches are suitable for operational optimisation. Linearisation exhibits superior computational efficiency compared to the neural network approach. The linearisation modelling approach together with the optimisation only required a few milliseconds for the calculation. The neural network approach needed 3 h for the calculation of the optimum with the genetic algorithm. The simulation of the neural network itself only required a few milliseconds. Hence, an improvement of the genetic algorithm is needed. However, the accuracy of linearisation falls short of that achieved by neural networks. The linearisation achieves a mean average percentage error from only 13%. In comparison the neural network's mean average error is 2.3%. Therefore, the linearisation must be improved. The impact using a piecewise linearisation on the results will be analysed in further research. [ABSTRACT FROM AUTHOR]

The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis. • A metro timetable optimization method based on improved genetic algorithm is developed. • The multi-node nonlinear model is developed to calculate the total energy consumption. • The developed optimization model has better energy-saving effects than previous methods. [ABSTRACT FROM AUTHOR]

Automated container terminals (ACTs) represent state-of-the-art facilities for container handling and are a current development trend. However, enhancing their operational efficiency while minimizing energy consumption remains a challenge. While metaheuristics are helpful in addressing container terminal problems, their capabilities are limited when used alone to tackle complex or integrated problems. Hybrid models can be more effective in facing these challenges. This research proposes a hybrid model, termed WOA + PSO, which combines the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) as a novel approach to address the integrated scheduling problem of automated quay cranes (AQCs), automated lift vehicles (ALVs), and automated stacking cranes (ASCs) in an ACT. Additionally, the WOA + PSO collaborates with a simulation model in a framework to become a simulation-based optimization approach. The performance of WOA + PSO is evaluated by comparing it with its base models, WOA and PSO, as well as a genetic algorithm (GA), through extensive experiments. The results show that WOA + PSO outperforms the others in achieving the objective of balancing operational efficiency and energy consumption. [ABSTRACT FROM AUTHOR]

• A multi-objective mixed integer program is proposed for the multi-load automated guided vehicles (m-AGVs) scheduling problem. • Real data informs the relationship between m-AGVs' energy consumption and payload weight, integrated in the proposed model. • The partial charging process of m-AGVs is incorporated into the scheduling model. The widespread implementation of modern logistics has led to the extensive use of multi-load automatic guided vehicles (m-AGVs) in automated sorting centers. This paper tackles one of the most challenging problems in this context, i.e., the scheduling of m-AGVs for express package handling. We comprehensively consider the loading and unloading delay of packages and energy consumption of m-AGVs, formulating the problem as a multi-objective mixed integer program (MO-MIP). To solve the proposed optimal scheduling problem, we apply the Non-dominated Sorting Genetic Algorithm (NSGA-II). A series of simulation experiments on three sorting centers have been further conducted to demonstrate the effectiveness and efficiency of the proposed model and solution algorithm. [ABSTRACT FROM AUTHOR]

This research develops a hybrid approach that integrates light gradient boosting machine (LightGBM) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the tunnel boring machine (TBM) performance during excavation. The TBM operational data are first extracted and meta -models are established to estimate the key TBM performance, including the penetration rate, over/under excavation ratio, and energy consumption. An optimization process is proposed by adopting NSGA-II and the technique for order preference by similarity to an ideal solution (TOPSIS) analysis to determine ideal operational parameters. The developed approach acts as a useful tool that assists tunnel construction automation and improves TBM performance under different in-situ conditions. Real data from a tunnel project in Singapore is utilized as a case study to examine the applicability and efficiency of the proposed approach. The results indicate that (1) The proposed meta -model provides reliable estimation with an average RMSE and MAE of 2.604 m m / m i n and 3.402 m m / m i n for TBM's penetration rate(O 1), 0.0211 and 0.0324 for over/under excavation ratio (O 2), and 15.512 k w h and 23.088 k w h for energy consumption (O 3), respectively. The prediction accuracy is better than the state-of-the-art methods; (2) The TBM's performance can be optimized by the proposed approach with an average improvement of 33.14 %, 1.32 %, and 17.95 % for O 1 to O 3 , respectively, and an overall improvement of 39.60 %; (3) The overall reliability of TBM operation improved after optimization with a significant reduction in data variance by 91.16 %, 76.92 %, and 97.35 % for O 1 to O 3 , respectively. This paper contributes to proposing a novel method that integrates LightGBM with NSGA-II in resolving the complex TBM operation problem by considering the major performance indexes including excavation efficiency, safety, and energy consumption during TBM operation. [ABSTRACT FROM AUTHOR]

The container relocation problem (CRP) is a combinatorial optimisation problem in which the sequence of container relocations must be determined to retrieve all containers from the yard while optimising a given objective. Prior to now, the primary objective of CRP was to minimise the number of container relocations; however, due to environmental concerns, optimising energy consumption is gaining importance. This criterion was not considered extensively in the literature about CRP; therefore, there is a lack of suitable methods to tackle this problem variant. Primarily, there is a lack of relocation rules (RRs), simple heuristics that efficiently solve large problems in negligible time. Unfortunately, RRs are challenging to design manually since this process requires significant domain knowledge and is time-consuming. Using hyperheuristics to evolve new RRs automatically is one way to circumvent this problem. In this study, we evolved new RRs that aim to minimise energy consumption using genetic programming. We consider both the scenario in which only energy consumption is optimised and a multi-objective scenario where energy consumption is optimised together with the number of container relocations. The proposed approach is compared with an existing approach from the literature that uses a genetic algorithm to design RRs. The results show that RRs designed using genetic programming perform significantly better than the existing method, especially in multi-objective scenarios. • Considering energy optimisation in container relocation problems. • Automated design of relocation rules for energy optimisation. • Automated design of rules for optimising energy and number of relocations. • Genetic programming achieves better performance than a genetic algorithm approach. • Using a flexible expression form is better than a fixed expression form. [ABSTRACT FROM AUTHOR]

The aim of this study is to optimize the exterior wall insulation of different orientations to further reduce the heating and cooling energy consumption of office buildings, and to analyze its economic, energy and carbon saving potential in China. A six-story office building was selected as the case building and its energy consumption was evaluated using TRNSYS, considering variations in latitude, window-to-wall ratio, aspect ratio, and exterior wall U -values of four orientations. In addition, the exterior wall insulation in four directions was optimized by coupling artificial neural network and genetic algorithm. The main results showed that in the 20°N to 40°N region of China, the GA-optimized exterior wall U -values were highest in the southern direction, followed by the eastern and western directions, and finally the northern direction. In addition, overall, the optimal combination of exterior wall U -values showed a trend of high at low latitudes and low at high latitudes, and increased with increasing WWR and decreased with increasing AR. Finally, compared to the exterior wall requirements for energy efficient buildings in GB 55015-2021, the optimized office buildings at low altitude in China showed a 3.26 %, 6.77 % and 6.59 % reduction in ALCC , total energy consumption and total carbon emissions respectively. • An economic optimization method for the exterior wall insulation based on wall orientation was proposed. • The optimal combination of exterior wall U -values was obtained. • The variation of the optimal exterior wall U -value with latitude was obtained by polynomial curve fitting. • The economic, energy and carbon saving potentials of the proposed method were analyzed by comparing with GB 55015-2021. [ABSTRACT FROM AUTHOR]

To address the low density of high-pressure gaseous hydrogen and evaporation issues associated with liquid hydrogen storage, cryogenic supercritical hydrogen storage method is presented in this paper. Based on a dual parallel mixed refrigerant cycle (DPMR) and a dual cascade mixed refrigerant cycle (DCMR), the density of supercritical hydrogen product is 70.74 kg/m3, closely approaching liquid hydrogen. The processes are simulated in Aspen HYSYS, optimized by genetic algorithm for lower specific energy consumption (SEC). Thermodynamic analysis is conducted to compare with hydrogen liquefaction processes, demonstrating the feasibility and superiority of proposed process. The results show that the DPMR process has an SEC of 6.422 kWh/kg H2 and exergy efficiency of 52.66%, while DCMR is 6.872 kWh/kg H2 and 49.24%. Additionally, the inclusion of ortho-para convertors in DPMR process is investigated to further analyze the impact of heat release for ortho-para conversion. Furthermore, a techno-economic analysis is conducted on three processes, validating the economic feasibility. The study indicates that cryogenic supercritical hydrogen storage system is of great significance to the development of high-density hydrogen storage technology. The DPMR and DCMR presented in this paper can significantly improve the energy efficiency and provide valuable information for the design of high-density hydrogen storage system. • Two high-density cryogenic supercritical hydrogen storage systems are proposed. • Mix refrigerant cycles are applied to reduce energy consumption. • The processes have lower SEC and higher FOM than conventional LH 2 processes. • The volume storage density of hydrogen can reach 70.74 kg/m3 in the systems. • OPC is considered to analyze the thermodynamic performance. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) have great potential to smooth mixed traffic flow. To focus on the fact that the driving styles of HDVs are time-varying, a new control framework based on time-varying model predictive control (MPC) is proposed for mixed traffic flow in longitudinal control. Firstly, a genetic algorithm can be employed to calibrate the parameters of the car-following model based on offline trajectory data, representing the long-term driving style of HDVs. Secondly, the short-term driving style is recognized through modifying the long-term driving style based on real-time trajectory data within a sliding window. Then, this paper seeks to design a time-varying MPC control framework to realize a smoother mixed traffic flow. Finally, compared to the baseline model, the method we proposed achieves the high-performance both in fuel consumption and driving comfort under various scenarios, and maintains fast online calculations. Besides, it also displays the significant potential of saving fuel consumption in a large-scale traffic system, reducing by over 11.28% with 17∼33% penetration rates of CAVs. • A car-following model with time-varying parameters for HDVs is proposed. • An updating mechanism for real-time parameters of the car-following model is presented. • A controller is designed based on time-varying MPC for smoothing mixed traffic flow. [ABSTRACT FROM AUTHOR]

Organic Rankine cycle (ORC) can improve engine power by recovering exhaust energy. This paper co-optimizes the engine-ORC combined system's power and NOx emission, with decision variables of the engine's excess air ratio, spark advance angle, as well as ORC's pump and expander speeds. Firstly, a simulation model of the combined system is established and validated. Then, the initial dataset is generated by the D-optimum Latin hypercube method and simulation model. The artificial neural network (ANN) prediction models of NOx emission and power are established based on these datasets. Finally, the co-optimization is conducted using the ANN prediction model and genetic algorithm. Focusing on maximizing the combined system's power results in an 18.30 % increase in power, and a significant reduction in brake-specific fuel consumption (BSFC) and brake-specific NOx (BSNOx) by 10.10 % and 71.30 %, respectively, compared to the unoptimized basis. Targeting the lowest BSNOx leads to a limited 1.20 % increase in power output; however, it results in a 19.50 % increase in BSFC. When optimizing for both system output and BSNOx, the output remains 13.5 % above the unoptimized basis. Meanwhile, up to 89.8 % of BSNOx can be eliminated with negligible deterioration in BSFC. This study could be used for engine performance enhancements. • A data-driven ANN prediction model of engine-ORC combined system is established. • ANN cooperates with GA to co-optimize Combined system's power and NOx emission. • The optimum engine and ORC parameters for the different goals are obtained. [ABSTRACT FROM AUTHOR]

The difficulty of cold start at low temperatures has been a major technical barrier to the commercialization of proton exchange membrane fuel cells (PEMFCs). In this context, this paper proposes a strategy that combines passive and active start to achieve fast, safe, and efficient start-up at −40 °C. Firstly, a simulation model of the PEMFC system is developed using experimental data to analyze the cold start performance and energy consumption of the stack. Moreover, a multi-stage cold start strategy for heating power and current is proposed to enable the cooperative control of the positive temperature coefficient (PTC) heater and the stack. To achieve this, the dual crucial but conflicting coefficients, heating energy consumption and start-up time, are used as the optimization objectives. And the genetic algorithm (GA) is used for optimization. The results show the weighting coefficient of the optimal cold start control strategy is 0.7. Compared to the passive cold start with weighting coefficient of 0, an increase in PTC heater energy consumption of 497 kJ reduces start-up time by 32.39 % and hydrogen required for reaction by 36.17 %. Compared with other active strategies, the start time is shortened by more than 115.4s without significantly improving the energy generated by stack. • The cold start model including the 140 kW PEMFC stack and TMS is presented. • A heating power and current control strategy based on stack temperature is raised. • The strategy combines active and passive cold start, and is optimized by GA. • Dual crucial objectives, heating energy consumption and cold start time are balanced. • The strategy achieved a cold start at −40 °C in less than 80 s. [ABSTRACT FROM AUTHOR]

With the introduction and imposition of the Carbon Intensity Indicator (CII) guidelines, more and more attention is being given to CII compliance of ships. A ship's normal operation will be affected when it has a CII rating of D or E. However, pursuing lower energy consumption may reduce economic benefits, which is a major concern for stakeholders. This paper proposes a method to maximum a ship's economic benefits while meeting the CII requirements. An optimization model is established by reallocating the proportions of different speed ranges and adjusting the corresponding average values. A genetic algorithm is adopted to solve the proposed model. The results of a case study based on real ship data demonstrate 5% to 8% improvements in annual full load distance after optimization while maintaining a CII rating of C or better in a given year. The proposed optimization model should be able to provide guidance for operational decision-making under increasingly stringent CII regulations. • Enabling CII compliance for previously non-compliant ships. • Exploring the potential economic benefits of energy-efficient ships. • Simultaneous fulfillment of CII requirements and economic benefits. • A significant improvement as demonstrated by real ship data. [ABSTRACT FROM AUTHOR]

• Combined cycle capable of offering three switchable energy supply modes was studied. • The set of the evaporator ensures the effective switching of different cycle modes. • Heat source in low temperature grade helps to desorb the work or basic solution. • Analysis was conducted from both thermodynamic and economic perspectives. • All cycle modes can maintain high comprehensive performance. The emergence of energy-related issues and the growing need for year-round energy supply has created a pressing demand for efficient cogeneration combined cycles. To this end, a new combined cycle using ammonia-water as the working fluid is proposed and studied from both thermodynamic and economic standpoints in this paper. The proposed cycle can effectively operate in power generation, power/refrigeration cogeneration and power/heating cogeneration modes. Parametric analysis and performance optimization of the proposed cycle are carried out, and the genetic algorithm method is employed for the optimization. Under the same initial conditions, the proposed cycle exhibits comprehensive power recovery efficiencies that are 58.11 %, 23.85 %, and 7 % relatively higher than those of the compared cogeneration cycles and Kalina cycle reported in the literature, respectively. The optimization results show that the maximum comprehensive power recovery efficiencies for the power/refrigeration, power/heating and power modes with heat source/cooling water temperature of 250℃/30℃, are 0.2271, 0.2658, and 0.1789, respectively. The associated power/refrigeration, power/heating, and power outputs are 221.1 kW/417.8 kW, 248.5 kW/650.9 kW, and 292.1 kW, respectively. Under these conditions, the overall annual unit cost of comprehensive power and the payback period of the cycle are 0.03947 $/kWh and 3.898 years. Results of the comprehensive analysis indicate that the proposed cycle can achieve satisfactory comprehensive performance for each mode, while also sufficiently recovering sensible heat source. [ABSTRACT FROM AUTHOR]

Dexamethasone (DXM) is a broadly used drug, which is frequently identified in the water environments due to its improper disposal and incomplete removal in wastewater treatment plant. The inability of conventional treatment processes of wastewater causes that researchers pay a great attention to study and develop effective wastewater treatment systems. This work deals with the study of integrated electro-peroxone/granular activated carbon (EP/US/GAC) process in the degradation of dexamethasone (DXM) from a water environment and the remediation of real pharmaceutical wastewater. Two approaches of response surface methodology based on central composite design (RSM-CCD) and artificial neural network based on algorithm genetic (ANN-GA) were employed for modeling and optimization of the process. Both the models presented significant adequacy for modeling and prediction of the process according to statistical linear and nonlinear metrics (R 2 = 0.9998 and 0.9996 and RMSE = 0.2128 and 0.1784 for ANN-GA and RSM-CCD, respectively). The optimization study provided the same outcomes for both ANN-GA and RSM-CCD approaches, where approximately complete DEX oxidation was achieved at pH = 9.3, operating time = 10 min, US power = 300 W/L, applied current = 470 mA, and electrolyte concentration = 0.05 M. A synergistic study signified that the EP/US/GAC process made an 82% synergy index as compared to the individual US and EP processes. The calculated energy consumption for the integrated process was achieved to be 2.79 kW h/gCOD. Quenching test by tert-butanol and p -benzoquinone revealed that HO• radical possessed the largest contribution in DEX degradation. The efficiency of EP/US/GAC process in the remediation of real pharmaceutical wastewater showed a significant decline in COD content (92% removal after 180 min), and the ratio of initial BOD/COD ratio of 0.27 was elevated up to 0.7 after 100 min treatment time. The performance stability of EP/US/GAC system showed no remarkable drop in removal efficiency, and leakage of lead ions from the anode surface was negligible and below WHO guideline for drinking water. Generally, this research work manifested that the integrated EP/US/GAC system elevated the degradation efficiency and can be proposed as a pretreatment step before biological treatment processes for the remediation of recalcitrant wastewaters. [Display omitted] • Modeling and optimizing DEX removal by EP/US/GAC process using RSM-CCD and ANN-GA. • A significant synergistic effect of EP/US/GAC process compared to separate processes. • Less electrical consumption by EP/US/GAC process compared to separate processes. • Improvement of BOD 5 /COD ratio in real pharmaceutical wastewater. • Hydroxyl radical has a major contribution in DEX degradation. [ABSTRACT FROM AUTHOR]

A photovoltaic thermal collector (PV/T) is incorporated into the combined cooling, heating, and power (CCHP) system to decrease primary energy consumption, operation cost and carbon dioxide emission. Six CCHP scenarios based on three strategies are investigated for the energy utilization of the CCHP system. Also, a non-dominated sorting genetic algorithm III using competition crossover and opposition-based learning (NSGA-III-CO) is proposed for the six scenarios. The competition crossover determines the starting point of search in the decision space, and more potential regions can be exploited sufficiently. The opposition-based learning (OBL) carries out random searches and facilitates the convergence of the population. In addition, a constraint handling approach (CHA) is presented to decrease the constraint violations of the infeasible individuals and turn them into feasible individuals, and it guarantees the feasibility of the population. Experimental results suggest that each CCHP scenario with PV/T is more efficient than the one with photovoltaic panel (PV) and the one neglecting PV/T and PV, and it achieves the lowest primary energy consumption, operation cost and carbon dioxide emission. Also, NSGA-III-CO and the other three algorithms are applied to the six CCHP scenarios with PV/T, and it obtains the highest hypervolume and coverage rate for each scenario. [Display omitted] • This paper studies dispatch of combined cooling, heating, and power system (CCHP). • The CCHP involves photovoltaic thermal collector. • An improved NSGA-III is proposed for CCHP. • The improved NSGA-III has exhibited good comprehensive performance for CCHP. • A constraint handling approach is presented to satisfy the constraints of CCHP. [ABSTRACT FROM AUTHOR]

Continuous growth of energy consumption for metro systems has raised the public's concern. Meanwhile, both operators and passengers pursue for efficient metro services, especially during peak hours. This paper aims at designing a bi-objective optimization model and a new solution approach so that the energy conservation of metro trains and less passenger waiting time can be achieved. Firstly, considering complex routes, a multi-particle train operation model with the objectives of punctuality, parking accuracy, energy efficiency and safety provided is established for the minimization of traction energy consumption through investigating the optimal force coefficients and coast positions, while suitable dwell times and headway times of metro trains are found trough building a timetable model, which balances the regenerative energy, transfer and non-transfer passenger waiting time. Secondly, a deep reinforcement learning (DRL) and non-dominated sorting genetic algorithm II (NSGA-II) based two-layer solution approach is introduced for model calculation. Finally, comparison shows that the proposed model can achieve the energy conservation of metro trains by 9.16% with passenger waiting time decreased by 15.88%. More experiments further demonstrate the superiority of the developed model and solution approach. [Display omitted] [ABSTRACT FROM AUTHOR]

Cloud computing, a technology providing flexible and scalable computing resources, faces a critical challenge in task scheduling, directly impacting system performance and customer satisfaction. The task scheduling problem's NP-completeness makes finding solutions difficult. To address this, researchers propose a hybrid algorithm combining the Grey Wolf Optimization Algorithm (GWO) and the Genetic Algorithm (GA). The hybrid GWO-GA algorithm targets multi-objective task scheduling in cloud computing, aiming to minimize makespan, energy consumption, and cost. Enhancements to the proposed algorithm involve leveraging the genetic algorithm's crossover and mutation operator. Furthermore, the GA-based GWO algorithm's faster convergence in large scheduling problems presents an advantage. Evaluation using the Cloudsim toolkit demonstrates the proposed algorithm's efficiency compared to existing methods. We have used both synthetic and real world data set. The results are verified using the Analysis of Variance (ANOVA) statistical tool. Experimental results showcase its effectiveness in minimizing makespan, energy consumption, and computational cost. Particularly, the proposed algorithm outperforms traditional GWO, GA, and PSO algorithms in terms of makespan, cost, and energy consumption, achieving reductions of 19%, 21%, and 15%, respectively, when compared to each approach. Additionally, it yields energy savings of 17%, 19%, and 23% compared to GWO, GA, and PSO, respectively, while reducing total scheduling costs by 13%, 17%, and 22%. These findings demonstrate the efficacy of the proposed algorithm in addressing the task scheduling problem in cloud computing environments. • A hybrid task scheduling strategy based on GA and GWO is proposed. • Introducing mutation and crossover to preserve wolf diversity and boost local search in the algorithm. • A novel fitness function is proposed considering multiple objectives simultaneously. • We have synthetic and real world data set for the simulation. • Results with CloudSim show the enhanced performance of proposed algorithm. [ABSTRACT FROM AUTHOR]

Intelligent applications have become essential for effectively managing energy consumption in public buildings. Energy management, especially for critical infrastructure buildings and public buildings, often serves as an example of sustainable practices since it tries to increase energy resilience through backup power systems. These structures were dealing with more difficulties, such as tight budgets, complicated regulations, behavioral issues, and others. Managing energy consumption in buildings is a multifaceted undertaking that entails grappling with non-uniform energy usage and a lack of established design guidelines for implementing energy-efficient and sustainable solutions. Consequently, analyzing energy utilization trends in public buildings and projecting future energy requirements is of paramount importance. This comprehension is indispensable to the identification and acknowledgment of energy consumption patterns in commercial and institutional buildings. Our research is highly significant for optimizing energy usage, reducing environmental impact, and making informed decisions in building management. We emphasize the importance of our predictive capabilities, sustainability, and life cycle assessment (LCA) techniques in achieving these objectives. Our hybrid model improves energy efficiency while also making a significant contribution to the larger objective of meeting sustainability standards in the built environment through thorough analyses of real-world data. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. In order to determine the number of energy consumption pattern clusters, a Self-Organizing Map (SOM) model was utilized in conjunction with Principal Component Analysis (PCA). The determination of clustering levels for each structure was executed by utilizing K-means in conjunction with a Genetic algorithm (GA). This approach enabled the identification of optimal clustering levels for the given structures, thereby facilitating effective analysis and interpretation of the data. The GA was specifically employed to determine the optimal centroid points for each cluster, thus optimizing the performance of the fitting model. Cluster analysis pattern extraction has made determining which buildings consume the most energy easier. As intelligent models for predicting energy usage, Convolutional Neural Networks (CNNs) and CNNs paired with a GA have also been used. The application of a GA to modify multiple settings of a CNN was conducted at this stage. The CNN model using a GA shows the method that was used provides higher accuracy and standard error compared to the conventional approach. It achieves a 94.01% accuracy on the training dataset and a 93.74% accuracy on the validation dataset with an error rate of 0.24 and 0.26 respectively. On the training dataset, the accuracy is 89.03% with a standard error of 0.3, whereas on the validation dataset, the accuracy is 88.91% with a 0.33 standard error. This research can help policymakers in the energy sector make better decisions regarding the timing of energy supply and demand for public buildings. [ABSTRACT FROM AUTHOR]