The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial c... [more]

This work explores five falling film evaporator (FFE) simulation approaches combined with energy consumption minimization strategies, namely Mechanical Vapor Recompression and Thermal Vapor Recompression (MVR and TVR, respectively). Global system analysis and advanced dynamic optimization strategies are then investigated to minimize steam consumption, the cost of steam, and the total annualized cost and to maximize product yield. The results indicate that higher TVR discharge pressures, or MVR compression ratios, along with higher feed temperatures, enhance evaporation but increase operational costs. The most economical option includes three evaporator effects with TVR to achieve 50% product dry mass content. However, for a 35% dry mass content, MVR becomes cost-effective with an 11% reduction in unit electricity prices or a simultaneous 7% drop in electricity prices and a 5% increase in gas-based steam prices. Furthermore, switching from milk powder production to milk concentrates lea... [more]

With the rapid development of intelligent manufacturing, the application of virtual reality technology to the optimization of workshop facility layout has become one of the development trends in the manufacturing industry. Virtual reality technology has put forward engineering requirements for real-time solutions to the Workshop Facility Layout Optimization Problem (WFLOP). However, few scholars have researched such solutions. Deep reinforcement learning (DRL) is effective in solving combinatorial optimization problems in real time. The WFLOP is also a combinatorial optimization problem, making it possible for DRL to solve the WFLOP in real time. Therefore, this paper proposes the application of DRL to solve the dual-objective WFLOP. First, this paper constructs a dual-objective WFLOP mathematical model and proposes a novel dual-objective DRL framework. Then, the DRL framework decomposes the WFLOP dual-objective problem into multiple sub-problems and then models each sub-problem. In or... [more]

The increasing use of fossil fuels has raised concerns about rising greenhouse gas emissions. Carbon capture, utilization, and storage (CCUS) is one of the most important technologies for achieving net zero carbon emissions. In oil reservoirs, fully understanding their geological characteristics, fluid characteristics, and pressure distribution and injecting CO2 in a reasonable scheme, some remaining oil can be recovered to improve oil recovery and even obtain certain economic benefits. In this paper, we investigate the effect of CCUS implementation in low-permeability reservoirs from both technical and economic aspects. First, based on the parameters of a low-permeability reservoir, a numerical simulation model of a reservoir with gas injection in a multi-stage fractured horizontal well at the top of the reservoir and oil recovery in a multi-stage fractured horizontal well at the bottom is established. Next, four cases of continuous CO2 injection, intermittent CO2 injection, CO2 injec... [more]

This paper presents an approach for optimizing the oil recovery factor by determining initial oil production rates. The proposed method utilizes the Q-learning method and the reservoir simulator (Eclipse 100) to achieve the desired objective. The system identifies the most efficient initial oil production rates by conducting a sufficient number of iterations for various initial oil production rates. To validate the effectiveness of the proposed approach, a case study is conducted using a numerical reservoir model (SPE9) with simplified configurations of two producer wells and one injection well. The simulation results highlight the capabilities of the Q-learning method in assisting reservoir engineers by enhancing the recommended initial rates.

A floatation nozzle can effectively transfer heat and dry without touching the substrate, and serves as a vital component for heat transfer to the substrate. Enhancing the heat transfer performance, and reducing its heat transfer unevenness to the substrate play an important role in improving product quality and reducing thermal stress. In this work, the effects of key structural parameters of the floatation nozzle on the heat transfer mechanism are systematically investigated by means of a numerical simulation of computational fluid dynamics. The findings demonstrate that the secondary vortex structure induced by the floatation nozzle with effusion holes increases heat transfer performance by 254.3% compared with the nozzle without effusion holes. The turbulent kinetic energy and temperature distribution between the jet and the target surface are affected by the jet angle and slit width respectively, which change the heat transfer performance of the float nozzle in different degrees.... [more]

Nanofluid Minimum Quantity Lubrication (NMQL) is a resource-saving, environmentally friendly, and efficient green processing technology. Therefore, this study employs Minimum Quantity Lubrication (MQL) technology to conduct milling operations on aerospace 7050 aluminum alloy using soybean oil infused with varying concentrations of MoS2 and MWCNTs nanoparticles. By measuring cutting forces, cutting temperatures, and surface roughness under three different lubrication conditions (dry machining, Minimum Quantity Lubrication, and nanofluid minimum quantity lubrication), the optimal lubricating oil with the best lubrication performance is selected. Under the conditions of hybrid nanofluid minimum quantity lubrication (NMQL), as compared to dry machining and Minimum Quantity Lubrication (MQL) processing, surface roughness was reduced by 48% and 36% respectively, cutting forces were decreased by 35% and 29% respectively, and cutting temperatures were lowered by 44% and 40%, respectively. Unde... [more]

Injection molding (IM) is an ideal technique for the low-cost mass production of moderately thick plane lenses (MTPLs). However, the optical performance of injection molded MTPL is seriously degraded by the warpage and sink marks induced during the molding process with complex historical thermal field changes. Thus, it is essential that the processing parameters utilized in the molding process are properly assigned. And the challenges are further compounded when considering the MTPL molding energy consumption. This paper presents a set of procedures for the optimization of injection molding process parameters, with warpage, sink marks reflecting the optical performance, and clamping force reflecting the molding energy consumption as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the S/N response shows that these three objectives cannot reach the optimal values simultaneously. Second, considering the experimental data scale, th... [more]

Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to the multi-output problem. Moreover, the data collection frequency of different processes is inconsistent, resulting in few effective data samples and strong nonlinearity. In this paper, we propose a multi-kernel multi-output support vector regression model optimized based on a differential evolutionary algorithm for the prediction of multiple types of energy consumption in tin smelting. Redundant feature variables are eliminated using the distance correlation coefficient method, multi-kernel learning is introduced to improve the multi-output support vector regression model, and a differential evolutionary algorithm is used to optimize the model hyperparameters. The validity and... [more]

In this paper, in order to reduce the spindle temperature rise and enhance the spindle heat dissipation capability, a top complementary heat conductive path of the spindle based on the IPTO algorithm was designed. In order to verify the heat dissipation effect of the heat conductive path, an experimental test platform was constructed. Experiments on the thermal characteristics of water-cooled and air-cooled heat conductive paths with different volume proportions were conducted to test the temperature rise of the spindle and analyze the effect of the heat conductive path with different volume proportions on the temperature distribution of the spindle. The heat conductive path with the optimal volume proportion was determined and the heat dissipation effect of the heat conductive path was verified.

Damage localization in GFRP (glass-fiber-reinforced polymer) composite plates is a crucial research area in marine engineering. This study introduces a feedback-based damage index (DI) combined with multi-label classification to enhance the accuracy of damage localization and address scenarios involving multiple damages. The research begins with the creation of a modal database for yachts’ GFRP composite plates using finite element modeling (FEM). A method for deriving a feedback-weighted matrix, based on the accuracy of the DI, is then developed. Sensitivity analysis reveals that the feedback DI is 50% more sensitive than the traditional DI, reducing false positives and missed detections. The associated feedback-weighted matrix depends solely on the structural shape, ensuring its transferability. To address the challenge for localizing multiple damages, a multi-label classification approach is proposed. The synergy between the feedback optimization and multi-label classification enabl... [more]

Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization with state-based learning strategy (APSO-SL) is put forward. In APSO-SL, the population distribution evaluation mechanism (PDEM) is used to evaluate the state of the whole population. In contrast to using iterations to just the population state, using the population spatial distribution is more intuitive and accurate. In PDEM, the population center position and best position for calculation are used for calculation, greatly reducing the algorithm’s computational complexity. In addition, an adaptive learning strategy (ALS) has been proposed to avoid the whole population’s premature convergence. In ALS, different learning strategies are adopted according to the population state to ensure the... [more]

This study aims to enhance energy efficiency by reducing parasitic losses in the engine cooling system through a new drive strategy involving a two-stage water pump and a variable electro-fan. The fuel consumption gain analysis focused on a vehicle with average characteristics typical of 1.0L hatchbacks in the Brazilian market and urban driving conditions. The methodology implemented aims to minimize power absorbed by the forced water circulation and thermal rejection, thereby reducing parasitic losses, particularly during low-speed urban driving, without causing air-side heat exchanger saturation. The results show a potential decrease of up to 80% in power absorbed by the cooling system, leading to an estimated fuel consumption saving of approximately 1.4% during urban driving cycles.

The selection of critical features from microarray data as biomarkers holds significant importance in disease diagnosis and drug development. It is essential to reduce the number of biomarkers while maintaining their performance to effectively minimize subsequent validation costs. However, the processing of microarray data often encounters the challenge of the “curse of dimensionality”. Existing feature-selection methods face difficulties in effectively reducing feature dimensionality while ensuring classification accuracy, algorithm efficiency, and optimal search space exploration. This paper proposes a hybrid feature-selection algorithm based on an enhanced version of the Max Relevance and Min Redundancy (mRMR) method, coupled with differential evolution. The proposed method improves the quantization functions of mRMR to accommodate the continuous nature of microarray data attributes, utilizing them as the initial step in feature selection. Subsequently, an enhanced differential evol... [more]

Improving the quality of the manufacturing process is an important goal of professional technicians. This study systematically explored the key success factors in the product-forming ability and visual optimization of 3D forming in the clay-shaping process through actual manufacturing and implementation. The purpose of this study was to identify the forming technology and technical indicators that can successfully achieve a high degree of completeness and maturity in the manufacturing process, increasing the shaping performance of the end products and enabling the evaluation of optimization. In this study, we evaluated soil shaping, material use, the manufacturing process, and product forming. The key success factors were studied and analyzed via expert interviews. The research and analysis were summarized into 4 dimensions with 48 sub-dimensions. These included: (1) soil material, with 12 sub-dimensions; (2) the design concept, with 12 sub-dimensions; (3) the prototype process, compri... [more]

This article explores the impact of operating parameters on the classification efficiency of a rotor classifier. Based on the experimental data of calcined petroleum coke classification, a single-factor experimental analysis is conducted to find the relationship between operating parameters and classification performance. The cut size becomes progressively smaller as the rotor speed and feeding speed increase, and progressively larger as the inlet air volume increases. Newton’s classification efficiency and classification accuracy decreased with the increase in feeding speed. The range analysis of the orthogonal experiment shows that the rotor speed and inlet air volume have significant effects on the classification performance, but the effect of feed speed is relatively weak. In addition, the optimal combination of operating parameters is obtained by optimizing the operating parameters. Newton’s classification efficiency under this combination is estimated, and the estimated value is... [more]

In this research work, an attempt has been made to address the heat and power integration opportunities for the process of the chlorination of benzene. This process produces a mixture of chlorobenzenes. To increase the production of the dichlorobenzene portion, the ratio of chlorine to benzene is typically 2:1. A process simulation model is designed using Aspen Plus for the production of 70,000 tons/year of dichlorobenzene via the reaction of liquid benzene with gaseous chlorine. Energy analysis is performed for the effective utilization of the utilities by networking the heat exchangers. This modification reduced the process heating and cooling requirements by 56.7% and 12.7%, respectively, and a reduction by 35.4% in the operating costs is achieved, while the annualized fixed cost increased by 9.6%; these changes resulted in savings in the total annual costs of about 10.9%.

Magnetic microwires with amorphous structures can present a unique combination of excellent magnetic softness and giant magnetoimpedance (GMI) effects together with reduced dimensions and good mechanical properties. Such unique properties make them suitable for various technological applications. The high GMI effect, observed in as-prepared Co-rich microwires, can be further optimized by postprocessing. However, unexpected magnetic hardening and a transformation of the linear hysteresis loop into a rectangular loop with a coercivity on the order of 90 A/m were observed in several Co-rich microwires upon conventional annealing. Several routes to improve magnetic softness and GMI effect in Fe- and Co-rich magnetic microwires are provided. We observed that stress annealing could remarkably improve the magnetic softness and GMI ratio of Co-rich microwires. Thus, almost unhysteretic loops with a coercivity of 2 A/m and a magnetic anisotropy field of about 70 A/m are achieved in Co-rich micr... [more]

The inherent volatility of PV power introduces unpredictability to the power system, necessitating accurate forecasting of power generation. In this study, a machine learning (ML) model based on Gaussian process regression (GPR) for short-term PV power output forecasting is proposed. With its benefits in handling nonlinear relationships, estimating uncertainty, and generating probabilistic forecasts, GPR is an appropriate approach for addressing the problems caused by PV power generation’s irregularity. Additionally, Bayesian optimization to identify optimal hyper-parameter combinations for the ML model is utilized. The research leverages solar radiation intensity data collected at 60-min and 30-min intervals over periods of 1 year and 6 months, respectively. Comparative analysis reveals that the data set with 60-min intervals performs slightly better than the 30-min intervals data set. The proposed GPR model, coupled with Bayesian optimization, demonstrates superior performance compar... [more]

Robotic friction stir welding (RFSW), with its wide application range, ample working space, and task flexibility, has emerged as a vital development in friction stir welding (FSW) technology. However, the low stiffness of serial industrial robots can lead to end-effector deviations and vibrations during FSW tasks, adversely affecting the weld quality. This paper proposes a dynamic dual particle swarm optimization (DDPSO) algorithm through a new comprehensive stability index that considers both the stiffness and vibration stability of the robot to optimize the installation position of complex space curve weldments, thereby enhancing the robot’s stability during the FSW process. The algorithm employs two independent particle swarms for exploration and exploitation tasks and dynamically adjusts task allocation and particle numbers based on current results to fully utilize computational resources and enhance search efficiency. Compared to the standard particle swarm optimization (PSO) algo... [more]

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]