In this paper, a hybrid three-stage methodology based on in vitro experiments, simulations, and metaheuristic optimization is presented to enhance the corrosion resistance of hydroxyapatite (HA)-coated magnesium implants in biomedical applications. In the first stage, we add cerium (Ce) to HA and present a new coating (named HA+Ce) to improve the resistance of the coating to corrosion. Then, various HA+Ce compounds with different factors (e.g., concentration, pH, immersion time, and temperature) are generated and their propensity for corrosion is examined in a physiological environment using EIS and DC polarization tests in a simulated body fluid solution. Eventually, a comprehensive dataset comprising 1024 HA+Ce coating samples is collected. In the second stage, machine learning using random forest (RF) is used to learn the relation between the input factors of the coating and its corrosion resistance. In the third stage, a metaheuristic algorithm based on the whale optimization algor... [more]

In today’s industrial landscape, the imperative of fault warning for equipment and systems underscores its critical significance in research. The deployment of fault warning systems not only facilitates the early detection and identification of potential equipment failures, minimizing downtime and maintenance costs, but also bolsters equipment reliability and safety. However, the intricacies and non-linearity inherent in industrial data often pose challenges to traditional fault warning methods, resulting in diminished performance, especially with complex datasets. To address this challenge, we introduce a pioneering fault warning approach that integrates an enhanced Coati Optimization Algorithm (ICOA) with a Bidirectional Long Short-Term Memory (Bi-LSTM) network. Our strategy involves a triple approach incorporating chaos mapping, Gaussian walk, and random walk to mitigate the randomness of the initial solution in the conventional Coati Optimization Algorithm (COA). We augment its sea... [more]

The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants. The pollution reduction and carbon emissions reduction in coal-fired power plants will be a key task in the future. In this paper, an optimization technique for the operation of an electrostatic precipitator is proposed. Firstly, the voltage-current model is constructed based on the modified dust charging mechanism; the modified parameters are trained through the gradient descent method. Then, the outlet dust concentration prediction model is constructed by coupling the mechanism model with the data model; the data model adopts the long short-term memory network and the attention mechanism. Finally, the particle swarm optimization algorithm is used to achieve the optimal energy consumption while ensuring stable outlet dust concentration. By training with historical data collected on site, accurate predictions of the secondary current and outlet... [more]

Mobile camera modules are manufactured by aligning and assembling multiple differently shaped part lenses. Therefore, selecting the part lenses to assemble from candidates (called cavities) and determining the directional angle of each part lens for assembly have been important issues to maximize production yield. Currently, this process is manually conducted by experts at the manufacturing site, and the manual assembly condition optimization carries the risk of reduced production yield and increased failure cost as it largely depends on one’s expertise. Herein, we propose an AI framework that determines the optimal assembly condition including the combination of part lens cavities and the directional angles of part lenses. To achieve this, we combine the genetic algorithm with convolutional bidirectional long-term short-term memory (C-BLSTM). To the best of our knowledge, this is the first study on lens module production finding the optimal combination of part lens cavities and direct... [more]

In order to improve the comprehensive utilization rate of industrial solid waste and the road quality, a novel low-carbon and environmental friendly soil stabilizer is proposed. In this study, steel slag (SS), carbide slag (CS), blast furnace slag (BFS), fly ash (FA), and desulfurized gypsum (DG) were used as raw materials to develop a multiple industrial solid waste-based soil stabilizer (MSWSS). The optimal mix ratio of the raw materials determined by D-optimal design was as follows: 5% SS, 50% CS, 15% BFS, 15% DG, and 15% FA. The 7-day unconfined compressive strength (UCS) of MSWSS-stabilized soil was 1.7 MPa, which was 36% higher than stabilization with ordinary portland cement (OPC) and met the construction requirements of highways. After 7 days of curing, the UCS of MSWSS-stabilized soil was significantly higher than that in the OPC group. X-ray powder diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) analysis indicated that the prominent... [more]

Cement concrete pavements are crucial to urban infrastructure, significantly influencing road safety and environmental sustainability with their anti-skid and noise reduction properties. However, while texturing techniques like transverse grooving have been widely adopted to enhance skid resistance, they may inadvertently increase road noise. This study addressed the critical need to optimize pavement textures to balance improved skid resistance with noise reduction. Tests were conducted to assess the influence of surface texture on skid resistance and noise, exploring the relationship between texture attributes and their performance in these areas. The investigation examined the effects of texture representation methods, mean profile depth, and the high-speed sideway force coefficient (SFC) on noise intensity and pavement skid resistance. The findings revealed that transverse grooves significantly improved the SFC, enhancing skid resistance. In contrast, longitudinal burlap drag, thro... [more]

This paper presents a bi-level inverse robust economic dispatch optimization model consisting of wind turbines and pumped storage hydropower (PSH). The inner level model aims to minimize the total generation cost, while the outer level introduces the optimal inverse robust index (OIRI) for wind power output based on the ideal perturbation constraints of the objective function. The OIRI represents the maximum distance by which decision variables in the non-dominated frontier can be perturbed. Compared to traditional methods for quantifying the worst-case sensitivity region using polygons and ellipses, the OIRI can more accurately quantify parameter uncertainty. We integrate the grid multi-objective bacterial colony chemotaxis algorithm and the bisection method to solve the proposed model. The former is adopted to solve the inner level problem, while the latter is used to calculate the OIRI. The proposed approach establishes the relationship between the maximum forecast deviation and the... [more]

To promote the development of green industries in the industrial park, a microgrid system consisting of wind power, photovoltaic, and hybrid energy storage (WT-PV-HES) was constructed. It effectively promotes the local consumption of wind and solar energy while reducing the burden on the grid infrastructure. In this study, the analytic hierarchy process (AHP) was used to decompose the multi-objective function into a single-objective function. The economic and environmental benefits of the system were taken as the objective function. Furthermore, the cuckoo search algorithm (CS) was used to solve the specific capacity of each distributed power source. Different scenarios were applied to study the specific capacity of microgrid systems. The results show that the equivalent annual cost of the WT-PV-HES microgrid system is reduced by 7.3 percent and 62.23 percent, respectively. The carbon disposal cost is reduced by 1.71 and 2.38 times, respectively. The carbon treatment cost is more sensi... [more]

In order to improve the erosion resistance of steel PDC (Polycrystalline Diamond Compact) bit under high-speed fluid flow conditions underground, it is necessary to develop a high-performance erosion-resistant coating. In this paper, laser cladding was used to prepare the new coating by modifying tungsten carbide with graphene. And the effects of tungsten carbide content and graphene content on the coating performance have been thoroughly studied and analyzed to obtain the optimal covering layer. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding tungsten carbide, the hardness has significantly improved. However, when the tungsten carbide content further increases more than 30%, the increase in hardness is limited. In addition, when the content of graphene is more than 0.3%, the branched structure becomes thicker. In detail, this is a phenomenon where the segregation of Cr, Si, and W becomes very obvious aga... [more]

Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the sho... [more]

Granulation is an important class of production processes in food, chemical and pharmaceutical manufacturing industries. In urea fertilizer manufacturing, fluidized beds are often used for the granulation system. However, the granulation processes release ammonia to the environment. Ammonia gas can contribute to eutrophication, which is an oversupply of nitrogen and acidification to the ecosystems. Eutrophication may cause major disruptions of aquatic ecosystems. It is estimated that global ammonia emissions from urea fertilizer processes are approximately at 10 to 12 Tg N/year, which represents 23% of overall ammonia released globally. Therefore, accurate modeling of the ammonia emission by the urea fertilizer fluidized bed granulation system is important. It allows for the system to be operated efficiently and within sustainable condition. This research attempts to optimize the model of the system using the particle swarm optimization (PSO) algorithm. The model takes pressure (Mpa),... [more]

Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations of the PM, there is a big computational effort that leads to a large controller execution time. The main motivation of this work is to decrease the computational effort and, consequently, the controller execution time. This paper proposes to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO and PM); the training data are obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple linear regression and the regression neural networks were considered the predicting models. This paper first proposes algorithms for collecting and... [more]

The advancement of photovoltaic module (PV) enhancer technology shows significant promise due to its rapid growth. Nevertheless, there remains a requirement for ongoing research to refine the evaluation techniques for this technology. In a prior investigation, the concept of the area and cost-effectiveness factor, denoted as FCAE, was introduced to analyze the economic impact of enhancing the PV through techniques such as reflectors or coolers. This metric relates the surface area and manufacturing expenses of a PV enhancer to its capacity for improving the PV output power, aiding in the comparison of different enhancer types. However, this assessment approach is costly, requiring a set of PVs without enhancers to be compared with an equal number of modules fitted with enhancers. This paper introduces a modified version of this metric, termed the modified area and cost-effectiveness factor (FMCAE), along with its minimum value (FMCAE,min), with the aim of reducing the assessment expens... [more]

The manufacturing and energy industry are typical complex large systems which cover a long cycle such as design [...]

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate.... [more]

The selection of growth substrate geometries for the mechanical dry transfer of carbon nanotubes to device substrates depends on the precision of the assembly equipment. Since these geometries play a decisive role in the overall efficiency of the process, an investigation of the most important geometry parameters is carried out. The substrate geometry affects the number of carbon nanotubes suspended during the growth process and the speed of mechanical assembly at the same time. Since those two criteria are interlinked and affect productivity, a meta-model for the growth and selection of the nanotubes is simulated and a time study of the resulting assembly motions is subsequently performed. The geometry parameters are then evaluated based on the total number of suspended carbon nanotubes and the throughput rate, measured in transfers per hour. The accuracy specifications are then taken into account. Depending on the overall accuracy that can be achieved, different offset angles and ove... [more]

Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms tradi... [more]

Coffee is not merely a refreshing beverage but also invigorates people, provides relaxation, contributes to human health, and fosters closer social connections. Coffee is one of the most widely consumed beverages worldwide and the most traded commercial commodity. Moreover, the rapid development of the Vietnamese coffee industry caused some concerns due to its insufficient performance and the fierce competition within the industry. It is significant to establish an efficient supply network; notwithstanding, supplier selection has always been a challenge for companies. Therefore, this paper employs a hybrid model to determine the supplier selection criteria, a vital factor for a manufacturer under practical operating conditions. Firstly, a combined model of Grey forecasting and the Grey Fourier series is applied to forecast future rainfall and temperature data for six consecutive years. Secondly, based on the criteria, strategies, and buyer requirements, the single-objective linear prog... [more]

Micro-pits are widely used in the aerospace and tribology sectors on cylindrical surfaces and electrochemical micromachining which are of great significance for the high material removal rate, absence of tool wear, and mechanical stress, while facing significant challenges such as stray corrosion and low machining efficiency. Aiming at the above problems, this paper proposes a comprehensive method called radial ultrasonic rolling electrochemical micromachining (RUREMM) in which an ultrasonic field has been added onto the cylindrical surface. First, a theoretical model was created to gain the rules of the formation and collapse of bubbles in the liquid medium. Second, to analyze the optimal size of the cathode electrode, the COMSOL5.2 simulation software was proposed to research the influence of the electric field on the different dimensions, and the influences of different parameters in RUREMM on material depth/diameter ratio and roughness are explored through processing experiments. R... [more]

As the power market system gradually perfects, the increasingly fierce competition not only drives industry development but also brings new challenges. Reactive power optimization is crucial for maintaining stable power grid operation and improving energy efficiency. However, the implementation of plant−grid separation policies has kept optimization costs high, affecting the profit distribution between power generation companies and grid companies. Therefore, researching how to effectively reduce reactive power optimization costs, both technically and strategically, is not only vital for the economic operation of the power system but also key to balancing interests among all parties and promoting the healthy development of the power market. Initially, the study analyzes and compares the characteristic curves of synchronous generators and DFIGs, establishes a reactive power pricing model for generators, and considering the randomness and volatility of wind energy, establishes a DFIG rea... [more]

The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the last decades. The need to explore this area is determined by the growing request for design and the optimization of more and more engineering problems in society, such as highway construction processes, food and agri-technologies processes, resource allocation problems, logistics and transportation systems, microarchitectures, suspension systems optimal design, etc. All of these matters refer to specific highly computational problems with a huge design space, hence the obvious need for evolutionary algorithms and frameworks, or platforms that allow for the implementing and testing of such algorithms and methods. This paper aims to comparatively analyze the existing software platforms and state-of-the-art multi-objective optimization algorithms and make a review of what features exist and what features might be included next as further developments in such tools, from a researcher’s perspective. A... [more]

The valorization of greenhouse gases, especially when focused on carbon dioxide, currently belongs to the main challenges of pro-environmental chemical processes. One of the important technologies in this field is dry methane reforming (DMR), leading to the so-called synthesis gas (CO + H2). However, to be efficient and economically viable, an active and stable catalyst is required. Ni-based systems can be recommended in this regard. This research aimed to investigate how nickel content can influence the activity of model NiOx/CeO2 catalysts in DMR. A series of NiOx/CeO2 samples of various nickel loadings (0−10 wt.%) were prepared through dry impregnation. The obtained samples were characterized through XRD, RS, N2-BET, DRIFT, SEM, UV/Vis-DR, and XPS. Nonlinear changes in surface properties of the investigated samples with increasing nickel concentration were found. The observed changes are mirrored both in the determined nickel speciation and in the corresponding catalytic activity. T... [more]

In response to the issue of the hydropower consumption of run-of-river hydropower stations in Southwest China, the district cooling system can provide regulation capacity for hydropower utilization and suppress fluctuations caused by the uncertainty of hydropower. The innovative method is to utilize the thermal characteristics of pipelines and buildings, as well as the thermal comfort elasticity to shift the cooling and electricity loads, which helps to consume the surplus hydroelectric power generation. Taking the minimum total cost of coal consumption in thermal power units, hydropower abandonment penalty, and the carbon trading cost as the objective function, models were established for power supply balance constraints, heat transport constraints, and unit output constraints. The hybrid integer linear programming algorithm was used to achieve the low-carbon economic dispatch of the electric-cooling system. The calculation examples indicate that compared to the traditional real-time... [more]

To eliminate or reduce stick−slip vibration in torsional vibration of the drilling string and improve the rate of penetration (ROP), a stick−slip vibration model of the drilling string considering the ROP was established based on the multidimensional torsional vibration model of the drilling string. The model was verified by simulation analysis. The characteristics of the drilling string stick−slip vibration in the three stages of stationary, slip, and stick were analyzed. This paper investigated the influence of rotary torque, rotary speed, and weight on bit (WOB) on stick−slip vibrations in the drill string. Based on this, the relationship between the drilling parameters and ROP was established. Drilling parameter optimization was completed for soft, medium-hard, and hard formations. Results showed that appropriately increasing torque and decreasing WOB can reduce or even eliminate stick−slip vibrations in the drill string and increase the ROP. The parameter optimization increased th... [more]

Drilling hazards can be significantly decreased by anticipating potential mud loss and then putting the right well control measures in place. Therefore, it is critical to provide early estimates of mud loss. To solve this problem, an enhanced WOA (Whale Optimization Algorithm) and a BiLSTM (Bidirectional Long Short Term Memory) optimization based prediction model of lost circulation prior to drilling has been created. In order to minimize the noise in the historical comprehensive logging data, a wavelet filtering technique was first used. Then, according to the nonlinear Spearman rank correlation coefficient between mud loss and logging parameter values from large to small, seven characteristic parameters were preferred, and the sliding window was used to extract the relevant data. Secondly, the number of neurons in the first and second hidden layers, the maximum training time, and the initial learning rate of the BiLSTM model were optimized using the enhanced WOA method. The BiLSTM ne... [more]