Oil production from tight oil reservoirs has become economically feasible because of the combination of horizontal drilling and multistage hydraulic fracturing. Optimal fracture design plays a critical role in successful economical production from a tight oil reservoir. However, many complex parameters such as fracture spacing and fracture half-length make fracturing treatments costly and uncertain. To improve fracture design, it is essential to determine reasonable ranges for these parameters and to evaluate their effects on well performance and economic feasibility. In traditional analytical and numerical simulation methods, many simplifications and assumptions are introduced for artificial fracture characterization and gas percolation mechanisms, and their implementation process remains complicated and computationally inefficient. Most previous studies on big data-driven fracturing parameter optimization have been based on only a single output, such as expected ultimate recovery, an... [more]

Powered roof support is equipped with a hydraulic control system to ensure its required load capacity. The main problem arising from powered roof support during exploitation is providing the necessary load capacity. A decrease in load capacity is mainly associated with internal and external leakage in the cylinders, mainly in the hydraulic props. The hydraulic prop’s role is to ensure stability for the powered roof support. A special double block with an additional pressure boost was developed to counter the props’ leakage phenomenon. Pressure loss is replenished based on the solution proposed here. For this purpose, bench tests were commenced, in which a prop with an internal leakage was used. The research included the analysis of the operation of a boosted double block. The results allowed us to assess whether the developed solution can be the subject of further research conducted in real conditions.

In response to the effects of climate change, China pledged to raise the share of non-fossil fuels in the primary energy consumption to around 25% and bring the total installed capacity of wind and solar power to over 1 [...]

The Daniudi gas field is located in the Ordos Basin’s northern section of the Yishan slope. The intertidal−subtidal depositional environment dominates the lithology of the Ma55 sub-member, resulting in a stable, thick-layered dark gray−gray−black limestone and lime dolomite. The stratum is stable laterally as well as dolomite, with an average thickness of 26.8 m. Fractures, dissolution expansion pores, and inter-crystalline dissolved pores are the primary reservoir space kinds, with a minor number of karst caves and fractures generated as well. The main distribution ranges for porosity and permeability are 1−8 percent and 0.01−1 mD, respectively. Low porosity, tightness, and ultra-low permeability are common characteristics, and a single well typically has no natural productivity. Production stimulation technologies like pre-fluid acid fracturing, compound sand addition acid fracturing, and multi-stage injection + temporary plugging volumetric acid fracturing have been gradually optimi... [more]

In this paper, renewable resources, namely photovoltaic panels (PV), are placed in a specific configuration to obtain the maximum reliability and availability of a microgrid and study the subcomponent-level reliability and availability. The reliability of components can be increased by trying different configurations of the components. We identify the preferred configuration used for the PV panels as bridged linked. The overall reliability of the microgrid is increased when component-wise reliability is considered. Even components are further divided into subcomponents, and the multiple faults of each component are considered. The method used for the reliability evaluation and availability study is Markov state transition modeling. The microgrid’s reliability and availability are plotted concerning time using Matlab. The optimization of reliability and availability is conducted through optimization techniques such as the genetic algorithm (GA) and artificial neural networks (ANN). The... [more]

Combined heat and power (CHP) plants are known as efficient technologies to reduce environmental emissions, balance energy costs, and increase total energy efficiency. To obtain a more efficient system, various optimization methods have been employed, based on numerical, experimental, parametric, and algorithmic optimization routes. Due to the significance of algorithmic optimization, as a systematic method for optimizing energy systems, this novel review paper is focused on the meta-heuristic optimization algorithms, implemented in CHP energy systems. By considering the applied objective functions, the main sections are divided into single-objective and multi-objective algorithms. In each case, the units’ combination is briefly detailed, the objective functions are introduced, and analyses are conducted. The main aim of this paper is to gather a database for the optimization of CHPs, demonstrate the effect of the applied optimization methods on the objective functions, and finally, in... [more]

The electrical system is undergoing a structural change, dissolving into a new concept of a distributed environment, based on renewable energies, demand management and resource sharing [...]

Traditional charging stations have a single function, which usually does not consider the construction of energy storage facilities, and it is difficult to promote the consumption of new energy. With the gradual increase in the number of new energy vehicles (NEVs), to give full play to the complementary advantages of source-load resources and provide safe, efficient, and economical energy supply services, this paper proposes the optimal operation strategy of a PV-charging-hydrogenation composite energy station (CES) that considers demand response (DR). Firstly, the operation mode of the CES is analyzed, and the CES model, including a photovoltaic power generation system, fuel cell, hydrogen production, hydrogen storage, hydrogenation, and charging, is established. The purpose is to provide energy supply services for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs) at the same time. Secondly, according to the travel law of EVs and HFCVs, the distribution of charging deman... [more]

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial po... [more]

As energy consumption constantly gains importance, it has become one of the major issues in managing logistics systems. However, it is ranked against other company priorities, and the rationalization for investing in energy needs to be justified by the savings achieved. A solution for reducing energy consumption via electric forklifts for performing docking operations at distribution centers, which requires no investments in infrastructure or equipment, is outlined in this paper. The solution is based on optimizing inbound dock door allocation, and the energy savings are quantified using a simulation model. A case study of a local FMCG distributor’s logistics center was conducted to collect the data and information needed for modeling inbound docking operations and performing simulation experiments. The optimal dock door allocation was obtained using a linear programming method using an MS Excel spreadsheet optimizer (Solver), while the simulation of the docking operations was carried... [more]

This paper presents a regenerative braking logic that aims to maximize the recovery of energy during braking without compromising the stability of the vehicle. This model of regenerative braking ensures that the regenerative torque of the electric motor (for front- and rear-wheel drive vehicles) or electric motors (for all-wheel drive vehicles equipped with one motor for each axle) is exploited to the maximum, avoiding the locking of the driving wheels and, subsequently, if necessary, integrating the braking with the traditional braking system. The priority of the logic is that of maximizing energy recovery under braking, followed by the pursuit of optimal braking distribution. This last aspect in particular occurs when there is an integration of braking and, for vehicles with all-wheel drive, also when choosing the distribution of regenerative torque between the two electric motors. The logic was tested via simulation on a front-, rear-, and all-wheel drive compact car, and from the s... [more]

In this article, a switched reluctance machine (SRM) with six phases and a misaligned segmental rotor is proposed. The segmental rotor has an internal 15-degree misalignment, allowing the SRM structure to be a one-layer 2D structure with a short flux path structure. The proposed SRM produces a relatively low torque ripple by exciting two phases simultaneously. Additionally, an optimization method is applied, allowing for the maximum torque position of one phase to be aligned with the zero-torque position of the adjacent phase. The finite element method (FEM) is used to analyze and design the proposed SRM and to simulate the proposed liquid cooling system. The static torque waveforms are analyzed, and the dynamic torque waveforms are simulated with a drive using SiC MOSFETs. Finally, a prototype is manufactured, and the experiment is performed to validate the design and simulation results.

Recently, there was a great focus on integrating renewable energy sources (RESs) into electrical power systems (hybrid systems) due to their many environmental and economic advantages. The output of most of these RESs is DC; some power electronic devices, including inverters, must be used to integrate these RESs into the electrical grid. Any maloperation, faults, or improper control in these power electronic devices will enormously affect these hybrid systems’ performance. This paper aims to mitigate the misfire and fire-through faults that occur at the switching of the inverter that connects three renewable sources: PV, wind, and the fuel cell to the grid. This mitigation of such inverter faults (misfire and fire-through) is performed through optimal tuning of the PI controller driving a dynamic voltage restorer (DVR) connected at the system’s AC side. The optimization technique used is particle swarm optimization (PSO). While mitigating these two inverter faults using the PI-PSO cont... [more]

Manual generation of test cases and scenario screening processes, during field architecture concept development, may produce a limited number of solutions that do not necessarily lead to an optimal concept selection. For more complex subsea field architectures, which might include processing modules for enhancing pressure and thermal management for the production network, the number of configuration cases and scenarios to evaluate can be extremely large and time and resource-consuming to handle through conventional manual design processes. This paper explores the use of evolutionary algorithms (EA) to automate case generation, scenario screening, and optimization of decentralized subsea processing modules during field development. An evaluation of various genetic operators and evolution strategies was performed to compare their performance and suitability to the application. Based on the evaluation results, an EA using structural uniform crossover and a gradient plus boundary mutation... [more]

Low-carbon transition pathways oriented from different transition targets would result in a huge variation of energy system deployment and transition costs. Hydrogen is widely considered as an imperative energy carrier to reach carbon neutral targets. However, hydrogen production, either from non-fossil power or fossil fuels with carbon capture, is closely linked with an energy supply system and has great impacts on its structure. Identifying an economically affordable transition pathway is attractive, and energy infrastructure is critical due to massive investment and long life-span. In this paper, a multi-regional, multi-period, and infrastructure-based model is proposed to quantify energy supply system transition costs with different low-carbon targets and hydrogen production alternatives, and China is taken as a case study. Results show that, fulfilling 2-degree and 1.5-degree temperature increase targets would result in 84% and 151% increases in system transition costs, 114% and 2... [more]

The integration of reaction and extractive distillation system is beneficial for reducing the process cost. A systematic method is proposed to identify the optimal solvent, solvent-to-feed ratio, extractive distillation sequence and reactor operating conditions based on the integration of reactor and extractive distillation. The non-key component matrix method is used to generate the extractive distillation sequences. The proposed method can be used to evaluate different solvents, solvent-to-feed ratios, as well as the extractive distillation sequences; the optimal extractive distillation parameters corresponding to the minimum total annualized cost (TAC) can be identified considering different operating conditions. For the selective hydrogenation of benzene to cyclohexene process, the minimum total annualized cost of unit desired product, 4.01 $·kmol−1, is achieved when the reactor’s inlet temperature is 333 K, solvent N-Formylmorpholine and sequence S2 are employed, and the solvent-t... [more]

Wind energy is a clean energy source and is receiving widespread attention. Improving the operating efficiency and economic benefits of wind power generation systems depends on more accurate short-term wind speed predictions. In this study, a new hybrid model for short-term wind speed forecasting is proposed. The model combines variational modal decomposition (VMD), the proposed improved seagull optimization algorithm (ISOA) and the kernel extreme learning machine (KELM) network. The model adopts a hybrid modeling strategy: firstly, VMD decomposition is used to decompose the wind speed time series into several wind speed subseries. Secondly, KELM optimized by ISOA is used to predict each decomposed subseries. The ISOA technique is employed to accurately find the best parameters in each KELM network such that the predictability of a single KELM model can be enhanced. Finally, the prediction results of the wind speed sublayer are summarized to obtain the original wind speed. This hybrid... [more]

In this study, a wavy microchannel heat sink with grooves using water as the working fluid is proposed for application to cooling microprocessors. The geometry of the heat sink was optimized to improve heat transfer and pressure loss simultaneously. To achieve optimization goals, the average friction factor and thermal resistance were used as the objective functions. Three dimensionless parameters were selected as design variables: the distance between staggered grooves, groove width, and groove depth. A modified Latin hypercube sampling (LHS) method that combines the advantages of conventional LHS and a three-level full factorial method is also proposed. Response surface approximation was used to construct surrogate models, and Pareto-optimal solutions were obtained with a multi-objective genetic algorithm. The modified LHS was proven to have better performance than the conventional LHS and full factorial methods in the present optimization problem. A representative optimal design sho... [more]

This study reports on the optimization of the operating conditions using response surface methodology and a comparative study of three promising technologies of cell disruption (bead milling, microwaves and ultrasound) to increase the lipid extraction from Nannochloropsis oceanica, Nannochloropsis gaditana and Tetraselmis suecica. Central composite designs were used for the optimization of ultrasound and microwave processes. The performance of the cell disruption processes in breaking down microalgae cells is dependent on the strain of microalgae. Microwaves (91 °C for 25 min) were the most efficient for the recovery of lipids from N. oceanica, reaching a lipid content of 49.0% dry weight. For N. gaditana, ultrasound process (80% of amplitude for 30 min) was the most efficient in terms of lipid recovery (21.7% dry weight). The two aforementioned processes are ineffective in disturbing T. suecica whatever the operating conditions used. Only the bead milling process at low flow feed rate... [more]

Simulated moving bed reactor (SMBR), a multicolumn multifunctional integrated reactor system, which can be exploited with on-site adsorptive separation to enhance conversion of equilibrium-limited reversible chemical reaction. In this article, for generality, a dimensionless SMBR model was developed and effects of five representative temperature distributions among different zones on the performance of an SMBR for reversible reaction in the general form of a reactant decomposed to two products were evaluated based on simultaneous maximization of unit throughput and product purity. Multipliers were applied to adjust some of the model parameters such that different operation modes can be compared under various conditions in the parametric space. The multiobjective optimization problems were solved using the non-dominated sorting genetic algorithm. All simulations were carried out using FORTRAN codes. The results showed that both kinetics and adsorptive separation play important roles in... [more]

A major part of Swiss fly ashes (FA) from municipal solid waste incineration (MSWI) are treated with the acid fly ash leaching process (FLUWA) in order to recover heavy metals prior to deposition. The FLUWA process uses scrub water from wet flue gas cleaning to leach heavy metals from FA. The leaching efficiency is strongly dependent on the leaching conditions (e.g., pH, Eh, L/S-ratio). This case study presents the optimization of the FLUWA process at the MSWI plant Linth, Switzerland, through determination of ideal process parameters for optimal metal recovery. By means of laboratory- and industrial-scale experiments, the process was adjusted towards a more efficient leaching of Zn, Pb, Cu, and Cd. This included the use of an oxidizing agent (hydrogen peroxide). Laboratory experiments proved to be a powerful tool for simulating process optimizations at industrial scale. An ideal leaching pH of 3.8 was determined and it was observed that the process stability is significantly influence... [more]

Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i)... [more]

A borehole heat exchanger (BHE) presents the most reliable source of geothermal energy for any object where the heat pump system is to be installed. The main objective of BHE optimization in a specific rock massive and for calculated heat consumption is to design a BHE with proper capacity and sustainable performance. One of the most important inputs for the preparation of such a model is the average outer air temperature on the site during the year. While the properties of the local rock massive are from the heat project lifetime view (tens of years) stable, the local average outer temperature fluctuates according to global climate changes. This article presents a study of the impact of climate changes on the dimensioning process of a BHE and consequently on its performance using data from a real installation at a highly climate-exposed site in the Czech Republic. During the dimensioning of a BHE, this study could help to better quantify the objective risks that result from climate ch... [more]

Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 31... [more]

Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor an... [more]