Minimum Total Energy Consumption Research Articles

ИССЛЕДОВАНИЕ УСТАНОВКИ С ИНФРАКРАСНЫМ НАГРЕВОМ ЗЕРНА

Thermal post-harvest processing of grain currently intended to improve its quality is highly energy-intensive and expensive. The development of new means of mechanization with infrared heating of grain, capable of reducing specific energy costs for heat treatment processes, as well as ensuring resource conservation, is a pressing and important scientific and technical problem. To reduce the cost of drying, disinfection and micronization of grain, as well as improve the quality of its heat treatment, we have developed an experimental model of an installation with infrared heating. The study of the developed installation was carried out in grain drying mode. The technological process of drying wheat grain with an initial moisture content of 18...19% was chosen as the object of study. The total specific energy consumption for grain drying was taken as an optimization parameter. The rational values of the selected factors were determined as a result of a study of the developed installation with infrared heating of grain: grain drying time t1 = 1000C; the temperature created by the infrared heater inside the installation casing is tin = 650C. Next, a series of experiments was carried out on the developed installation in the above modes. In each experiment, measurements were taken of the heating temperature of the grain, the percentage of reduction in grain moisture content and the throughput of the installation were determined, and the total specific energy consumption was recorded. The heating temperature of the grain was in the range of 37 - 38.5 ºС, while the moisture content of the grain decreased by 2.56%. The developed installation provides the required quality of grain drying, maintaining a throughput of about 800 kg/h and a minimum total specific energy consumption, which amounted to 4342.3 kJ h/kg.

An assessment of the impact of building envelope design on the tradeoff between embodied and operating energy

The building envelope encompasses an important share of construction materials and may dictate the embodied energy in buildings. This paper performs parametric analysis to evaluate the affect of several envelope design parameters on the operational and embodied energy consumption of the building. These parameters include southern wall glazing ratio, northern wall glazing ratio, glazing type, and wall assembly. The wall assembly design variables in turn include interior insulation thickness, interior insulation material, exterior insulation material, exterior insulation thickness, and outmost layer material. The other parts of the wall assembly remained constant in all simulation runs. A single store residential building with 150 m2 built-up area is considered as a case study to perform calculations. Based on the simulation results, Increasing the glazing ratio in the southern and northern walls from 0 to 80% results in a 12.7% and 15.7% increase in the total energy consumption, respectively. Moreover, employing triple pane windows raises the embodied energy compared to the double pane windows by 27.5%. However, they result in a 4.44% reduction in total energy consumption due to their better thermal insulation properties. Finally simultaneous variation of the glazing ratio and wall assembly reveled the minimum possible total energy consumption during the life cycle of the building can be obtained by providing aerogel as insulation material with 100 mm thickness in the interior insulation layer and 60 mm in the exterior insulation layer, fiber cement panels as the outmost layer and triple pane windows.

A multi-objective optimization energy management strategy for marine hybrid propulsion with waste heat recovery system

To improve the efficiency of marine hybrid propulsion systems (HPS) and make full of the potential of the waste heat recovery system, this paper presents a powerplant scheme which includes marine HPS with thermoelectric generator (TEG) and organic rankine cycle (ORC) system. The original energy management strategy (EMS) based on the multi-objective optimization algorithm is proposed, which considers the objectives of minimum total energy consumption, maximum output energy of TEG and ORC, and the optimal engine efficiency. The EMS is validated with different initial states of charge of the battery, and the influence of weight factors in the EMS on the system performance is also investigated. Additionally, the interactions between various ship driving cycles and performance of TEG and ORC system are analyzed. Compared with the traditional engine propulsion, the maximum energy saving of the HPS is 13.74%, while the efficiency of the engine and ORC system has been improved by 1.53% and 1.17%, respectively. This paper provides a new algorithm of waste heat recovery system regulation and gives a valuable reference for EMS optimization in marine HPS.

Federated Deep Reinforcement Learning for Energy-Efficient Edge Computing Offloading and Resource Allocation in Industrial Internet

Industrial Internet mobile edge computing (MEC) deploys edge servers near base stations to bring computing resources to the edge of industrial networks to meet the energy-saving requirements of Industrial Internet terminal devices. This paper considers a wireless MEC system in an intelligent factory that has multiple edge servers and mobile smart industrial terminal devices. In this paper, the terminal device has the choice of either offloading the task in whole or in part to the edge server, or performing it locally. Through combined optimization of the task offload ratio, number of subcarriers, transmission power, and computing frequency, the system can achieve minimum total energy consumption. A computing offloading and resource allocation approach that combines federated learning (FL) and deep reinforcement learning (DRL) is suggested to address the optimization problem. According to the simulation results, the proposed algorithm displays fast convergence. Compared with baseline algorithms, this algorithm has significant advantages in optimizing the performance of energy consumption.

A digital-twin-based adaptive multi-objective Harris Hawks Optimizer for dynamic hybrid flow green scheduling problem with dynamic events

In many hybrid flow shop production environments, dynamic events (DEs) often occur and seriously impact production schedules. However, the traditional model rarely considers DEs, which makes the model seriously inconsistent with the actual situation. In addition, to reduce carbon emissions, there is an urgent need to carry out green optimization scheduling of production workshops, especially in emerging digital workshops. First, this paper proposes a dynamic digital-twin-based Multi-Objective Hybrid Flowshop Green Scheduling Model with DEs (MOHFGSM-DEs) to fill the above gaps. The MOHFGSM-DEs model’s bi-objectives are the makespan minimization and digital workshop total energy consumption minimization. In addition, the MOHFGSM-DEs model takes account of representative DEs in the actual dynamic production process, including controlled processing time, device dynamic reconfiguration events, and workpiece reworking events, which makes it more adaptive in practical production. Secondly, an Adaptive Multi-Objective Dynamic Harris Hawks Optimizer (AMODHHO) is presented with DEs parameters perceived by the digital twin of the workshop, adjusting the scheduling scheme adaptively in real time to address the MOHFGSM-DEs model efficiently. AMODHHO combines a nonlinear optimization strategy on balancing the exploration and exploitation of Harris Hawks Optimizer (HHO) and introduces the crossover operator of Genetic Algorithm (GA) to improve its optimal global ability. Moreover, a digital-twin-driven dynamic encoding methodology containing many optimization strategies is designed based on problem-specific characteristics. Finally, numerical experiments and application case comparisons are performed among AMODHHO, SPEA2, and NSGA-II. Results show that AMODHHO is superior to SPEA2 and NSGA-II regarding comprehensive performance. Therefore, the proposed model and AMODHHO are feasible to solve the real-world MOHFGSM-DEs adaptively. Furthermore, it performs dynamic adaptive adjustment for the actual scheduling schemes according to the real-time DEs the digital twin perceives.

Numerical model of an industrial refrigeration system for condensation temperature optimisation

Refrigeration plants in the food industry have a key role, yet are very energy-intensive, which poses a serious problem given the current steep rise of energy prices. In this framework, energy consumption minimisation represents a paramount goal for plant managers, yet most are loathe to test new control strategies in real-life plants, lest normal operation be disrupted and food be spoiled as a consequence. In this view, numerical models able to demonstrate the reduction in energy consumption which can be achieved with suitable conduction strategies, especially in the case when evaporative condensers are employed, appear the ideal tool to provide the manager with an estimation of the potential savings and spur them into adopting such strategies. In this work one such model is developed for the primary loop of the refrigeration plant of a warehouse for food storage located in northern Italy. The choice of the model type is discussed at length, as are modelling issues related to all main components of the loop. The model has been validated with operational data from the real-life plant and employed to determine the optimal condensation pressure corresponding to the minimum total energy consumption according to ambient conditions. The method and model can be applied to other, similar plants in order to minimise their energy consumption.

Dynamic energy-efficient scheduling of multi-variety and small batch flexible job-shop: A case study for the aerospace industry

In the context of the global implementation of carbon peaking and carbon neutrality, energy-aware production scheduling has attracted world-wide attention. The optimal scheduling problem for a multi-variety and small-batch dynamic flexible job shop scheduling is challenging in aerospace industry. This paper considers an energy-efficient scheduling problem with multi-resource constraints of multi-variety and small-batch dynamic flexible job shop (α-shop for short). First, it formulates a mathematical model of energy efficient α-shop with the objectives of minimum total energy consumption (TEC), makespan and machining cost. Next, a novel multi-objective bi-population differential artificial bee colony (BDABC) algorithm is proposed to solve the model. Then, the design of experiment Taguchi method is performed to calibrate the parameter setting of the proposed algorithm. Finally, comparative experiments of the proposed algorithm with other popular algorithms such as large-scale multi-objective optimization framework (LSMOF), large-scale multi-objective competitive swarm optimizer (LMOCSO), as well as some known algorithms including multi-objective evolutionary algorithm based on decomposition (MOEA/D), multi-objective particle swarm optimization (MOPSO) and multi-objective artificial bee colony (MOABC) are carried out to assess the effectiveness of the proposed algorithm in an aerospace plant of China based on a number of well-recognized metrics. The experimental results demonstrate that our proposed algorithm is effective reported for this real-life scheduling problem.

Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks

In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be regarded as a new type of multi-antenna system. We formulate a total energy consumption minimization problem by a joint optimization of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient while taking into account the constraints of communication resources and computing resources. This formulated problem is a non-convex optimization problem due to the high coupling of optimization variables, which is NP-hard to obtain its optimal solution. To address the above challenging problems, block coordinate descent (BCD) technique is employed to decouple the optimization variables to solve the problem. Specifically, the joint optimization problem of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient is divided into three subproblems to solve by applying BCD. Then, the decoupled three subproblems are optimized alternately by using successive convex approximation (SCA) and difference-convex (DC) programming until the convergence is achieved. Numerical results verify that our proposed algorithm is superior in reducing total energy consumption compared to other benchmarks.

Energy-Aware Coded Transmission Strategy for Hierarchical Cooperative Caching Networks

With the rapid development of millions more edge base stations (EBSs) and billions of user devices in the future networks, the desire for low-energy system design and transmission strategy will be even more compelling. In this letter, we investigate the energy consumption issue of hierarchical cooperative caching networks (HCCNs) in which both EBSs and users are capable of storing content data in their local caches. A three-stage coded transmission strategy in HCCNs is proposed, and the closed expression of energy consumption of each stage is derived. In addition, we formulate a total energy consumption minimization problem with the constraint of EBSs’ cache sizes, and provide an algorithm to obtain the optimal cache placement matrix. We demonstrate by numerical results that, our optimized three-stage coded transmission strategy can achieve lower energy consumption than various multicast transmission strategies under the different users’ cache sizes.

A novel discrete elephant herding optimization for energy-saving flexible job shop scheduling problem with preventive maintenance

Recently, energy-saving scheduling issues have attracted more and more attention in the manufacturing field. Meanwhile, in practical production, maintenance planning is viewed as a vital task in the workshop. However, the existing literature about energy-saving scheduling problems rarely consider the effect of preventive maintenance. Therefore, this paper investigates an energy-saving flexible job shop scheduling problem with preventive maintenance. A mathematical model is proposed considering the minimization of total energy consumption. To solve the problem, a novel discrete elephant herding optimization algorithm (NDEHO) is proposed according to the problem’s characteristics. To test the NDEHO’s performance, the Taguchi design of experiment approach is adopted to get the best combination of parameters in the algorithm. Numerical experiments are conducted based on twenty-four instances, including four benchmark instances and twenty randomly generated instances. Computational data indicate that NDEHO outperforms other compared algorithms for solving the considered problem.

A multi-input single-output thermal management system design for liquid metal batteries

Liquid metal battery, with three-liquid-layer structure and high operating temperature, is a novel battery technology that shows great potential in grid energy storage. However, unlike lithium-ion batteries, liquid metal batteries are supposed to be heated to a high temperature before operation. Both temperature uniformity among cells and total energy consumption minimization is demanded in the preheating process. In this paper, an efficient thermal management controller design is proposed to optimize the temperature uniformity and energy consumption. Different from previous work, this design replaces the conventional single-input single-output controller with a multi-input single-output controller, which samples temperatures at different locations to control the heating rate. Experimental results show that the final controller error reaches a value of 1.2%. Based on the thermal management system design, a preheating strategy is further proposed to achieve the optimal balance between temperature uniformity and energy consumption. Compared with common strategies, the maximum temperature difference is reduced by 13.7% to 49.85 °C, and the energy consumption is reduced by 6.7% of the total module energy to 6.33 kWh. It is demonstrated in this study that the proposed thermal management system and heating strategy essentially improves the heating process of liquid metal battery stack.

Energy Efficient Platooning of Connected Electrified Vehicles Enabled by a Mixed Hybrid Electric Powertrain Architecture

The platooning of electrified vehicles can improve vehicle performance by enhancing energy efficiency and safety. Nevertheless, studies focusing on control strategies for platoons usually ignore or over-simplify the powertrain in the vehicle dynamics model. Advanced powertrain control strategies for electrified vehicles show that efficient operation of the powertrain depends on the current vehicle status, such as the torque demand, vehicle speed, and battery state of charge. In addition, the operation of individual powertrains can affect platoon-level performance, and the relationship between powertrain operation and vehicle status can be highly nonlinear. In studies of platoons, the evaluation of vehicle performance usually requires the vehicle to converge to a static operating point or to follow a simple operating pattern. Ideal cases can help in the understanding and improvement of vehicle performance but may be less beneficial for applications in realistic cases that are more complex, e.g. in military operations. In this study, a platooning optimization problem is formulated for optimal performance between two locations, given the terrain grade and soil properties along the path. A high-fidelity powertrain model is constructed and embedded in a detailed platoon model. The drive schedule is specifically designed for a platoon of vehicles with an innovative hybrid electric powertrain to achieve minimum total energy consumption. Results show that the approach is able to reduce energy consumption and headway keeping errors.

Energy-efficient allocation for multiple tasks in mobile edge computing

Mobile edge computing (MEC) allows a mobile device to offload tasks to the nearby server for remote execution to enhance the performance of user equipment. A major challenge of MEC is to design an efficient algorithm for task allocation. In contrast to previous work on MEC, which mainly focuses on single-task allocation for a mobile device with only one task to be completed, this paper considers a mobile device with multiple tasks or an application with multiple tasks. This assumption does not hold in real settings because a mobile device may have multiple tasks waiting to execute. We address the problem of task allocation with minimum total energy consumption considering multi-task settings in MEC, in which a mobile device has one or more tasks. We consider the binary computation offloading mode and formulate multi-task allocation as an integer programming problem that is strongly NP-hard. We propose an approximation algorithm and show it is a polynomial-time approximation scheme that saves the maximum energy. Therefore, our proposed algorithm achieves a tradeoff between optimality loss and time complexity. We analyze the performance of the proposed algorithm by performing extensive experiments. The results of the experiments demonstrate that our proposed approximation algorithm is capable of finding near-optimal solutions, and achieves a good balance of speed and quality.

Energy-Minimized Partial Computation Offloading for Delay-Sensitive Applications in Heterogeneous Edge Networks

Mobile Devices (MDs) support various delay-sensitive and computation-intensive applications. Yet they only have limited battery energy and computing resources, thereby failing to totally run all applications. A mobile edge computing (MEC) paradigm has been proposed to provide additional computation, storage, and networking resources for MDs. Servers in MEC are often deployed in both macro base stations (MBSs) and small base stations (SBSs). Thus, it is highly challenging to associate resource-limited MDs to them with high performance, and realize partial computation offloading among them for minimizing total energy consumption of an MEC system. To tackle these challenges, this work proposes a novel computation offloading approach for delay-sensitive applications with multiple separable tasks in hybrid networks including MDs, SBSs, and an MBS. To achieve it, this work formulates total energy consumption minimization as a constrained mixed integer non-linear program. To solve it, this work designs an improved meta-heuristic optimization algorithm called <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> article swarm optimization based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> enetic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning (PGL), which integrates strong local search capacity of a particle swarm optimizer, and genetic operations of a genetic algorithm. PGL jointly optimizes task offloading among MDs, SBSs, and MBS, users’ connection to SBSs, MDs’ CPU speeds and transmission power, SBSs and MBS, and bandwidth allocation of available channels. Simulations with real-world data collected from Google cluster trace demonstrate that PGL significantly outperforms other existing methods in total energy consumption of an entire system.

Quantum tunicate swarm algorithm based energy aware clustering scheme for wireless sensor networks

Wireless Sensor Network (WSN) is represented by a group of sensor nodes with limited battery capacity deployed in a target region for data collection purposes. Energy is treated as a major issue from the design of WSN as it mainly affects the overall functioning of the network. A commonly employed energy-efficient mechanism is clustering which groups the nodes into several groups of clusters. Earlier works reported that clustering is treated as an NP hard issue which can be addressed by soft computing techniques. In this way, this article focuses on the design of Quantum Tunicate Swarm Algorithm Based Energy Aware Clustering (QTSA-EAC) scheme for WSN. The presented QTSA-EAC technique mainly attempts to effectively manage the available energy among the nodes in WSN. In addition, the QTSA is derived by the incorporation of quantum computing concepts as to the traditional TSA. Moreover, the QTSA-EAC technique derives a fitness function with the minimization of total energy consumption in the networks. The inclusion of multiple parameters in the cluster head (CH) selection process helps to accomplish energy efficiency and maximum lifetime of WSN. The performance assessment of the QTSA-EAC technique is performed and the results reported that the presented model is effective, improves lifetime, reduce energy exploitation, and delay over the other methods.

Systematic investigation of SO2 adsorption and desorption by porous powdered activated coke: Interaction between adsorption temperature and desorption energy consumption

Porous carbon materials have been widely used for the removal of SO 2 from flue gas. The main objective of this work is to clarify the effects of adsorption temperature on SO 2 adsorption and desorption energy consumption. Coal-based porous powdered activated coke (PPAC) prepared in the drop-tube reactor was used in this study. The N 2 adsorption measurements and Fourier transform infrared spectrometer analysis show that PPAC exhibits a developed pore structure and rich functional groups. The experimental results show that with a decrease in adsorption temperature in the range of 50–150 °C, the adsorption capacity of SO 2 increases linearly; meanwhile, the adsorption capacity of H 2 O increases, resulting in the increase in desorption energy consumption per unit mass of adsorbent. The processes of SO 2 and H 2 O desorption were determined by the temperature-programmed desorption test, and the desorption energies for each species were calculated. Considering the energy consumption per unit of desorption and the total amount of adsorbent, the optimal adsorption temperature yielding the minimum total energy consumption of regeneration is calculated. This study systematically demonstrates the effect of adsorption temperature on the adsorption–desorption process, providing a basis for energy saving and emission reduction in desulfurization system design.

Optimal Control Strategies for Demand Response in Buildings under Penetration of Renewable Energy

The penetration rates of intermittent renewable energies such as wind and solar energy have been increasing in power grids, often leading to a massive peak-to-valley difference in the net load demand, known as a “duck curve”. The power demand and supply should remain balanced in real-time, however, traditional power plants generally cannot output a large range of variable loads to balance the demand and supply, resulting in the overgeneration of solar and wind energy in the grid. Meanwhile, the power generation hours of the plant are forced to be curtailed, leading to a decrease in energy efficiency. Building demand response (DR) is considered as a promising technology for the collaborative control of energy supply and demand. Conventionally, building control approaches usually consider the minimization of total energy consumption as the optimization objective function; relatively few control methods have considered the balance of energy supply and demand under high renewable energy penetration. Thus, this paper proposes an innovative DR control approach that considers the energy flexibility of buildings. First, based on an energy flexibility quantification framework, the energy flexibility capacity of a typical office building is quantified; second, according to energy flexibility and a predictive net load demand curve of the grid, two DR control strategies are designed: rule-based and prediction-based DR control strategies. These two proposed control strategies are validated based on scenarios of heating, ventilation, and air conditioning (HVAC) systems with and without an energy storage tank. The results show that 24–55% of the building’s total load can be shifted from the peak load time to the valley load time, and that the duration is over 2 h, owing to the utilization of energy flexibility and the implementation of the proposed DR controls. The findings of this work are beneficial for smoothing the net load demand curve of a grid and improving the ability of a grid to adopt renewable energies.

A green model for identical parallel machines scheduling problem considering tardy jobs and job splitting property

• A bi-objective green model has been formulated to solve the problem of scheduling parallel machines considering job splitting property. • The first objective function minimizes the total number of tardy jobs while the second objective function is minimization of total energy consumption. • An augmented ε-constraint method has been deployed for solving small-scale problems. • To solve large-scale problems, an efficient simulated annealing algorithm has been developed. • A harmony search algorithm has also been applied to examine the quality of the proposed SA algorithm. • Random generated problems have been used to compare the results of three deployed algorithms. In most organizations, especially order-oriented ones meeting deadlines are crucial. Job shop environments could be mentioned as an example where decision makers are try to schedule all jobs in such a way that tardy jobs (TJ) are minimized. Indeed, manufacturers are received customers’ orders and required to deliver them no later than the determined due dates. Otherwise, penalties should be paid to customers and it can lead to a huge amount of financial loss. Also, regarding global warming and climate changes manufacturers should redesign their processes from an eco-friendly perspective. Motivated by these issues, in this paper, a green bi-objective model has been formulated to solve the problem of scheduling parallel machines considering TJ and job splitting property (JSP). In the proposed model, the first objective function minimizes the total number of TJ while the second objective function is minimization of total energy consumption. An augmented ε-constraint method has been deployed for solving small-scale problems. However, to solve large-scale problems, an efficient Simulated Annealing (SA) algorithm has been developed while a Harmony Search (HS) algorithm has also been applied to examine the quality of the proposed SA algorithm. Random generated problems have been used to compare the results of three deployed algorithms. Results approved that the proposed SA algorithm outperforms others significantly. In particular, SA solved the problems sooner than others while its solutions were closer to solutions of the augmented ε-constraint method.

Energy Consumption Optimization Algorithm for Full-Duplex Relay-Assisted Mobile Edge Computing Systems

In order to alleviate the pressure of terminal devices to deal with the big-data and low-delay services, a resource allocation algorithm is studied for mobile edge computing networks with full-duplex relays. Firstly, the constraints of the maximum task latency, the maximum computing ability of users, and the maximum transmit power of users and relays are considered for achieving total energy consumption minimization by jointly optimizing the relay selection and subcarrier allocation factor, user task offloading coefficient, and the transmission power of users and relays. Secondly, based on the alternating iteration method and the variable-substitution approach, the originally non-convex problem is decomposed into two convex subproblems, which are solved by using the interior-point method and Lagrange dual theory, respectively. Simulation results show that the proposed algorithm has low energy consumption.

A game-based deep reinforcement learning approach for energy-efficient computation in MEC systems

Many previous energy-efficient computation optimization works for mobile edge computing (MEC) systems have been based on the assumption of synchronous offloading, where all mobile devices have the same data arrival time or calculation deadline in orthogonal frequency division multiple access (OFDMA) or time division multiple access (TDMA) systems. However, the actual offloading situations are more complex than synchronous offloading following the first-come, first-served rule. In this paper, we study a polling callback energy-saving offloading strategy, that is, the arrival time of data transmission and task processing time are asynchronous. Under the constraints of task processing time, the time-sharing MEC data transmission problem is modeled as the total energy consumption minimization model. Using the semi-closed form optimization technology, energy consumption optimization is transformed into two subproblems: computation (data partition) and transmission (time division). To reduce the computational complexity of offloading computation under time-varying channel conditions, we propose a game-learning algorithm, that combines DDQN and distributed LMST with intermediate state transition (named DDQNL-IST). DDQNL-IST combines distributed LSTM and double-Q learning as part of the approximator to improve the ability of processing and predicting time intervals and delays in time series. The proposed DDQNL-IST algorithm ensures rationality and convergence. Finally, the simulation results show that our proposed algorithm performs better than the DDQN, DQN and BCD-based optimal methods.