Syngas fermentation via the Wood-Ljungdahl (WL) pathway is a promising approach for converting gaseous pollutants (CO and CO2) into high-value commodities. Because the WL involves several enzymes with trace metal components, it requires an adequate supply of micronutrients in the fermentation medium for targeted bioprocessing such as bioethanol production. Plackett-Burman statistical analysis was performed to examine the most efficient trace elements (Ni, Mg, Ca, Mn, Co, Cu, B, W, Zn, Fe, and Mo) and their concentrations for Clostridium ljungdahlii on ethanol production. Overall, 1.5 to 2.5 fold improvement in ethanol production could be achieved with designed trace element concentrations. The effects of tungsten and copper on ethanol and biomass production were determined to be the most significant, respectively. The model developed was statistically significant and has the potential to significantly decrease the cost of trace element solutions by 18−22%. This research demonstrates th... [more]

This paper aims to explore torque optimization control issue in the turning of EV (Electric Vehicles) with motorized wheels for reducing energy consumption in this process. A three-degree-of-freedom (3-DOF) vehicle dynamics model is used to analyze the total longitudinal force of the vehicle and explain the influence of torque vectoring distribution (TVD) on turning resistance. The Genetic Algorithm-Particle Swarm Optimization Hybrid Algorithm (GA-PSO) is used to optimize the torque distribution coefficient offline. Then, a torque optimization control strategy for obtaining minimum turning energy consumption online and a torque distribution coefficient (TDC) table in different cornering conditions are proposed, with the consideration of vehicle stability and possible maximum energy-saving contribution. Furthermore, given the operation points of the in-wheel motors, a more accurate TDC table is developed, which includes motor efficiency in the optimization process. Various simulation re... [more]

The circulating cooling water system is widely used in various industrial production fields, and its operating cost largely depends on external factors, such as ambient temperature and working medium flow. Considering the relative elevation of the heat exchanger, this study establishes a total system operation cost analysis and optimization model based on the superstructure method. The model uses ambient dry bulb temperature, ambient wet bulb temperature, and working medium flow as random variables. Water supply temperature is adopted as the decision variable, and the minimum operating cost of the system is used as the objective function. An analysis of the effect of the three random variables on the operation cost shows that the effect of ambient dry bulb temperature on the operation cost is negligible, and the effect of ambient wet bulb temperature and working medium flow on the operation cost is significant. In addition, a control equation of water supply temperature is established... [more]

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers i... [more]

Solar Tracking Systems are useful to increase the generation efficiency of photovoltaic technology, mainly for concentration technology, where dual-axis is required on account of the high accurate alignment to the Sun. Even when there exists a strong relation between tracking error and energy efficiency, multiple technological and research developments have sought to solve these problems independently. The present research proposes a novel concurrent design methodology for optimizing the overall performance of two-axis trackers, allowing to keep a balance between the tracking error and the energy consumption from the design stage, from an optimization approach. The concurrent approach was implemented to design a Solar Tracker as a solar monitoring system, was compared with four commercial systems, obtaining a similar pointing accuracy with a mixed tracking error of 0.0942°. The system has the best energy balance, consuming only 0.9641% of the energy generated for the tracking action, b... [more]

We analyzed the potential of thermoelectrics for electricity generation in a combined heat and power (CHP) waste heat recovery system. The state-of-the-art organic Rankine cycle CHP system provides hot water and space heating while electricity is also generated with an efficiency of up to 12% at the MW scale. Thermoelectrics, in contrast, will serve smaller and distributed systems. Considering the limited heat flux from the waste heat source, we investigated a counterflow heat exchanger with an integrated thermoelectric module for maximum power, high efficiency, or low cost. Irreversible thermal resistances connected to the thermoelectric legs determine the energy conversion performance. The exit temperatures of fluids through the heat exchanger are important for the system efficiency to match the applications. Based on the analytic model for the thermoelectric integrated subsystem, the design for maximum power output with a given heat flux requires thermoelectric legs 40−70% longer th... [more]

This paper studies energy consumption management of seawater Reverse Osmosis (RO) desalination plants to maintain and enhance the Voltage Stability (VS) of Power Systems (PS) with Photovoltaic (PV) plant integration. We proposed a voltage-based management algorithm to determine the maximum power consumption for RO plants. The algorithm uses power flow study to determine the RO plant power consumption allowed within the voltage-permissible limits, considering the RO process constraints in order to maintain the desired fresh water supply. Three cases were studied for the proposed RO plant: typical operation with constant power consumption, controlled operation using ON/OFF scheduling of the High-Pressure Pumps (HPPs) and controlled operation using Variable Frequency Drive (VFD) control. A modified IEEE 30-bus system with a variable load was used as a case study with integration of three PV plants of 75 MWp total power capacity. The adopted 33.33 MW RO plant has a maximum capacity of 200,... [more]

Economic Dispatch (ED) problems have been solved using single-objective optimization for so long, as Grid System Operators (GSOs) previously only focused on minimizing the total production cost. In modern power systems, GSOs require not only optimizing the total production cost but also, at the same time, optimizing other important objectives, such as the total emissions of the greenhouse gasses, total system loss and voltage stability. This requires a suitable multi-objective optimization approach in ensuring the ED solution produced is satisfying all the objectives. This paper presents a new multi-objective optimization technique termed Multi-Objective Immune-Commensal-Evolutionary Programming (MOICEP) for minimizing the total production cost and total system loss via integrated Economic Dispatch and Distributed Generation installation (ED-DG). This involved the application of a weighted-sum multi-objective approach that combined with an optimization technique called Immune-Commensal... [more]

The analytical solutions of complex dynamic PRO systems pose challenges to ensuring that maximum power can be harvested in stable, rapid, and efficient ways in response to varying operational environments. In this paper, a boosted particle swarm optimization (BPSO) method with enhanced essential coefficients is proposed to enhance the exploration and exploitation stages in the optimization process. Moreover, several state-of-the-art techniques are utilized to evaluate the proposed BPSO of scaled-up PRO systems. The competitive results revealed that the proposed method improves power density by up to 88.9% in comparison with other algorithms, proving its ability to provide superior performance with complex and computationally intensive derivative problems. The analysis and comparison of the popular and recent metaheuristic methods in this study could provide a reference for the targeted selection method for different applications.

This paper presents research results on heat pipe numerical models as optimization of heat pipe heat exchangers for intensification of heat exchange processes and the creation of heat exchangers with high efficiency while reducing their dimensions. This work and results will allow for the extension of their application in passive and low-energy construction. New findings will provide a broader understanding of how heat pipes work and discover their potential to intensify heat transfer processes, heat recovery and the development of low-energy building engineering. The need to conduct research and analyses on the subject of this study is conditioned by the need to save primary energy in both construction engineering and industry. The need to save primary energy and reduce emissions of carbon dioxide and other pollutants has been imposed on the EU Member States through multiple directives and regulations. The presented numerical model of the heat pipe and the results of computer simulati... [more]

CO2-based enhanced gas recovery (EGR) is an appealing method with the dual benefit of improving recovery from mature gas reservoirs and storing CO2 in the subsurface, thereby reducing net emissions. However, CO2 injection for EGR has the drawback of excessive mixing with the methane gas, therefore, reducing the quality of gas produced and leading to an early breakthrough of CO2. Although this issue has been identified as a major obstacle in CO2-based EGR, few strategies have been suggested to mitigate this problem. We propose a novel hybrid EGR method that involves the injection of a slug of carbonated water before beginning CO2 injection. While still ensuring CO2 storage, carbonated water hinders CO2-methane mixing and reduces CO2 mobility, therefore delaying breakthrough. We use reservoir simulation to assess the feasibility and benefit of the proposed method. Through a structured design of experiments (DoE) framework, we perform sensitivity analysis, uncertainty assessment, and opti... [more]

As a result of lightning strikes, pollution, and ice, overhead distribution wires might be short-circuited and trip. As a result, researchers have developed a new lightning protection composite insulator. There is still a need to test its pollution and icing performance. Based on the finite element and field test method, this paper studies the electric field distribution and AC (Alternating Current) breakdown characteristics of polluted novel lightning protection insulators under icing conditions. Firstly, the finite element calculated results show that this novel insulator’s electric field distribution is different from that of a conventional insulator. The locations with sizeable electric fields are located in the insulation section, and the electric field in the arrester section is tiny. In addition, when the insulator surface is covered with ice, there is an increase in the electric field along the surface and pin electrodes. Compared with the dry conditions, when there is an ice l... [more]

This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system’s point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is... [more]

Parafoil trajectory directly affects the power generation of a high-altitude wind power generation (HAWPG) device. Therefore, it is particularly important to optimize the parafoil trajectory and then to track it effectively. In this paper, the trajectory of the parafoil at high altitudes is optimized and tracked in a comprehensively parameterized manner. Both the complex dynamic characteristics of the parafoil and the dexterous demand of the high-altitude controller are considered. Firstly, the trajectory variables and control signals are parameterized as Lagrange polynomials in terms of the corresponding values at the selected nodes. Then, the Radau pseudospectral method (PSM) is employed to reformulate the original dynamic trajectory optimization problem into a static nonlinear programming (NLP) problem. By doing so, the parameterized optimal trajectory, which has the maximum net power generation, can be obtained. To attenuate the strong nonlinear, multivariable and coupling characte... [more]

Through the integration of multiple energy carriers with related technologies, multi-energy systems (MES) can exploit the synergies coming from their interplay for several benefits towards decarbonization. In such a context, inclusion of Power-to-X technologies in periods of excess renewable electricity supply, removes the need for curtailment of renewable electricity generation. In order to achieve the environmental benefits of MES without neglecting their economic feasibility, the optimal design problem is as crucial as challenging and requires the adoption of a multi-objective approach. This paper extends the results of a previous work, by investigating hydrogen-based non-conventional storage for PV power in the eco-energetic optimization of an MES. The system under study consists of a reversible fuel cell (r-SOC), photovoltaic (PV), electric heat pump, absorption chiller and thermal storage, and allows satisfying the multi-energy needs of a residential end-user. A multi-objective l... [more]

The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying... [more]

Window design affects the overall performance of a building. It is important to include window design during the initial stages of a project since it influences the performance of daylight and thermal comfort as well as the energy demand for heating and cooling. The Norwegian building code facilitates two alternative methods for achieving a sufficient daylight, and only guidelines for adequate indoor thermal comfort. In this study, a typical Norwegian residential building was modeled to investigate whether the criteria and methods facilitate consistent and good performance through different scenario changes and furthermore, how the national regulations compare to European standards. A better insulated and more air-tight building has usually a lower annual heating demand, with only a marginal decrease in the daylight performance when the window design is unchanged. A more air-tight construction increases the risk of overheating, even in cold climates. This study confirms that a revision... [more]

Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization (MPSO) along with equilibrium optimizer (EO), termed as MPSO-EO. The proposed approach is tested firstly on 15 benchmark test functions, and then it is implemented to solve the UCP under two test systems. The results are basically compared to that of standard EO and previously applied optimization techniques in solving the UCP. In test system 1, the load demand is deterministic. The proposed technique is in the best three solutions for the 10-unit system with cost savings of 309.95 USD over standard EO and for the 20-unit system it shows the best results over all algorithms in comparison with cost savings of 1951.5 USD over standard EO. In test system 2, the load demand is considered stocha... [more]

The aim of the ISORC/OPTIMISER project is to increase and improve the use of solar thermal energy in district heating networks. One of the main tasks of the project is to develop an optimization tool for the sizing and operation of a solar district heating network. This is the first optimization tool using an open-source interface (Julia, JuMP) and solver (Ipopt) to solve nonlinear problems. This paper presents the multi-period optimization problem which is implemented to consider the dynamic variations in a year, represented by four typical days, with an hourly resolution. The optimum is calculated for a total duration of 20 years. First, this paper presents the modeling of the different components of a solar district heating network production plant: district network demand, storage and three sources, i.e., a fossil (gas) and two renewable (solar and biomass) sources. In order to avoid prohibitive computational time, the modeling of sources and storage has to be fairly simple. The mu... [more]

The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange betwee... [more]

The Hechuan gas field is one of the tight gas reservoirs with the highest formation water salinity in China. The content of metal ions, such as calcium, magnesium, iron, and barium, is as high as 20 g/L. Severe scales in near-wellbore reservoir blocks the gas and liquid flow paths, affecting the normal production of gas wells. The analysis of scale samples shows that the scale compositions in the Hechuan gas field are complex, which are composed of calcium carbonate, calcium sulfate, barium sulfate, iron salt, silicate, and other inorganic scales. To dissolve these scales, 14 kinds of laboratory self-made chelating acids named AST-01 to AST-14, sequentially, were evaluated by the descaling rate, in which the chelating acid AST-01 was selected with a dissolution rate of 77.7%. Meanwhile, the optimal concentration and reaction time of AST-01 were investigated, and the concentrations of the corrosion inhibitor, the iron ion stabilizer, and surfactants were also optimized. Then, a chelatin... [more]

The generation of energy by conventional systems leads to several environmental issues. Fuel Cell (FC), being a new renewable energy source, has emerged as one of the promising alternatives to obtain clean and efficient energy generation. This paper highlights the power quality enhancement of the grid connected FC through a boost converter and 25 level Cascaded H-Bridge (CHB) Multi-Level Inverter (MLI) using the classical PID controller. To drive the MLI connected to the grid for governing the Point of Common Coupling (PCC) voltage between the FC and the grid, two PID controllers have been utilized. The conventional evolutionary techniques such as Particle Swarm Optimization (PSO) and Squirrel Search Algorithm (SSA) are implemented to tune the PID controllers for dynamic operations. To further enhance the convergence speed of computation and precision of the classical techniques used, an Improved Squirrel Search Algorithm (ISSA) has been proposed in this work. The grid connected power... [more]

Real-time energy management strategy (EMS) plays an important role in reducing fuel consumption and maintaining power for the hybrid electric vehicle. However, real-time optimization control is difficult to implement due to the computational load in an instantaneous moment. In this paper, an Approximate equivalent consumption minimization strategy (Approximate-ECMS) is presented for real-time optimization control based on single-shaft parallel hybrid powertrain. The quadratic fitting of the engine fuel consumption rate and the single-axle structure characteristics of the vehicle make the fitness function transformed into a cubic function based on ECMS for solving. The candidate solutions are thus obtained to distribute torque and the optimal distribution is got from the candidate solutions. The results show that the equivalent fuel consumption of Approximate-ECMS was 7.135 L/km by 17.55% improvement compared with Rule-ECMS in the New European Driving Cycle (NEDC). To compensate for the... [more]

Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solv... [more]

In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% is... [more]