Industrial Engineering Applications Research Articles

β‐Galactosidase: Insights into source variability, genetic engineering, immobilisation and diverse applications in food, industry and medicine

β‐Galactosidases are crucial enzymes that hydrolyse oligosaccharides and polysaccharides with terminal β‐1,4‐glycosidic bonds. Though the traditional application of β‐Galactosidases has been to catalyse the breakdown of lactose in dairy products, its application extends beyond the production of lactose‐free products since variants capable of facilitating lactose condensation and exhibiting galactosyl transferase activity are extensively utilised for the synthesis of prebiotic galacto‐oligosaccharides. This review analyses β‐Galactosidase in multiple aspects, including sources, classification, characterisation, immobilisation, genetic engineering and applications in terms of whey treatment, biofuel production, production of lactose‐free dietary product, synthesis of galacto‐oligosaccharides and the early detection of cellular senescence and tumours.

Understanding cavitation bubble collapse and rebound near a solid wall

The collapse of cavitation bubbles is known for its intense and aggressive behavior, producing concentrated bursts of energy that can cause erosion, noise, and damage to metallic surfaces and mechanical equipment. However, this concentrated energy also offers significant potential for various applications in biomedical science, industrial engineering, and related technologies. This study presents a comprehensive numerical investigation of the collapse and rebound of cavitation bubbles near a wall using a conservative, compressible multiphase flow model. Laser-generated cavitation bubbles growing, collapsing, and rebounding in distilled water near a wall at three standoff distances were experimentally studied to validate the numerical method. The results show good agreement between the bubble shapes and radii from the numerical simulations and experiments. Additionally, velocity of microjets and thermodynamics of the bubble collapse were validated against the reference data in the literature. Subsequently, the pressure and temperature distributions and jet shapes were examined in detail, exploring the impact of standoff distances ranging from 0.2 to 1.8. A quantitative analysis of the pressure and temperature produced on the wall, as well as the jet velocity and bubble movement during the collapse, was systematically conducted. Jet velocities observed ranged from 30 m/s to 130 m/s, and numerical simulations were compared with experimental data from the literature, showing good agreement. Maximum temperature and pressure induced at the wall center can reach up to 616.2 K and 139.5 MPa, respectively. The detailed analyses in this study provide significant insights into the behavior of cavitation bubbles and the effect of standoff distance on their collapse and rebound near a wall.

Highly-efficient filtered hierarchical identification algorithms for multiple-input multiple-output systems with colored noises

Multiple-input multiple-output (MIMO) systems have extensive applications in industrial processes and systems engineering. This letter applies the filtering identification idea to establish a filtered identification model and investigate a filtered auxiliary model-based recursive least squares (F-AM-RLS) algorithm for parameter identification of MIMO systems with colored noises. To improve the computational efficiency, this work further proposes a four-stage filtered auxiliary model-based recursive least squares (4S-F-AM-RLS) algorithm by means of the hierarchical identification principle. Then, by incorporating the forgetting factor, a four-stage filtered auxiliary model-based forgetting factor recursive least squares (4S-F-AM-FF-RLS) algorithm is given to improve the convergence speed and the estimation accuracy. Additionally, the computational complexity analysis of the proposed algorithms indicates that the 4S-F-AM-RLS algorithm effectively reduces the computational burden and improves computational efficiency. Finally, the effectiveness of the F-AM-RLS, 4S-F-AM-RLS and 4S-F-AM-FF-RLS algorithms is validated through a numerical example.

Three-dimensional titanium mesh-based flow electrode capacitive deionization for salt separation and enrichment in high salinity water

Flow electrode capacitive deionization (FCDI) is a highly promising desalination technique known for its exceptional electrosorption capacity, making it suitable for efficient salt separation in high salinity water. However, the unsatisfactory charge transfer process between the flow electrode and current collector severely curtails the salt separation and enrichment performance of the FCDI device. To address this issue, three-dimensional titanium mesh (3D-TM) was proposed as a novel current collector for FCDI device, which significantly amplifies the charge transfer area and exhibits excellent salt separation performance. The 3D-TM current collector promotes the electron transfer, charge percolation, and ion migration processes through the electroconvection generated by the turbulence effect on the flow electrode. In the specific case of the 20-mesh 3D-TM, which is composed of 12 stacking layers of titanium mesh, the remarkable average salt removal rate and charge efficiency were achieved 5.06 μmol cm−2 min−1 and 92.9 % under an appropriate applied voltage of 2.0 V, respectively. Dramatically, the desalination performance maintained above 76.4 % over 100 desalination cycles at 2.0 V, demonstrating the exceptional cyclic stability of the 3D-TM FCDI cell. In the seawater desalination, the 3D-TM FCDI cell exhibited an impressive salt removal efficiency of 97.5 % (from 34.2 g L−1 to 0.84 g L−1) for 1 L East China seawater at 2.0 V for 24 h. For lithium-ion enrichment, the FCDI continuous desalting system achieved an astonishing concentration of 17.3 g L−1 for Li+ ions enrichment from an initial concentration of 1.30 g L−1, obtaining the average salt treating rate of 23.6 g m−2h−1 and charge efficiency of 80.0 %. Moreover, the lithium-sodium ions and lithium-magnesium ions enrichments were both conducted, yielding an enriched concentration of 10.4 g L−1 and 7.30 g L−1 for Li+ ions, respectively. These findings highlight the enormous potential of FCDI technology in industrial engineering applications, further establishing it as a highly viable solution.

Correction: Topp-Leone Cauchy Family of Distributions with Applications in Industrial Engineering

Correction: Topp-Leone Cauchy Family of Distributions with Applications in Industrial Engineering

An overview on Finite Different Method (FDM) in multilayer thermal diffusion problem

Numerous mathematical models have been developed for thermal diffusion through single-layer materials, while further developments are needed for multi-layer materials. Diffusion processes through a multilayer material are of interest in industrial, applied thermodynamics, physics, electrical and civil engineering applications. The proposed scheme is easily applicable in various fields, demonstrating that Finite Different Methods (FDMs) are flexible, simple to implement and help to represent multilayer materials even without associating them with other numerical methods. The numerical method studied is a FDM in a civil engineering application. Through the study of a multilayer wall, its criticalities are highlighted, and a possible implementation is proposed.

Topp-Leone Cauchy Family of Distributions with Applications in Industrial Engineering

The goal of this research is to create a new general family of Topp-Leone distributions called the Topp-Leone Cauchy Family (TLC), which is exceedingly versatile and results from a careful merging of the Topp-Leone and Cauchy distribution families. Some of the new family’s theoretical properties are investigated using specific results on stochastic functions, quantile functions and associated measures, generic moments, probability weighted moments, and Shannon entropy. A parametric statistical model is built from a specific member of the family. The maximum likelihood technique is used to estimate the model’s unknown parameters. Furthermore, to emphasize the new family’s practical potential, we applied our model to two real-world data sets and compared it to existing rival models.

Scatter search for capacitated minimum spanning tree

We develop a scatter search algorithm for the classical capacitated minimum spanning tree (CMST) problem. We address both the homogeneous and the more difficult heterogeneous variant of the problem. The CMST is central in a wide range of network design applications in industrial engineering, routing and logistics, and communication networks. Since the problem is NP-Complete, heuristic solution methods are often required to find high-quality solutions within practical time limits. Research efforts have been focused on complex algorithm designs combining advanced neighborhood search techniques with exact solution methods or hybrids of genetic algorithms with various alternative search techniques and likewise supplemented with optimization procedures. In this study we go back to the roots and offer an alternative algorithm that competes with the best algorithms in the literature and both avoids complicated artifacts and is simpler to implement. Key features of the proposed algorithm include an oscillation neighborhood search method characterized by two classes of neighborhoods and an evolutionary search procedure based on a scatter search framework.

Lean six sigma Application in Engineering Industry

lean six sigma Application in Engineering Industry

Accurate identification of cashmere and wool fibers based on enhanced ShuffleNetV2 and transfer learning

Recognizing cashmere and wool fibers has been a challenging problem in the textile industry due to their similar morphological structure, chemical composition, and physicochemical properties. Traditional manual methods for identifying these fibers are inefficient, labor-intensive, and inaccurate. To address these issues, we present a novel method for recognizing cashmere and wool fibers using an improved version of ShuffleNetV2 and Transfer Learning, which we implement as a new cashmere and wool classification network (CWCNet).The approach leverages depthwise separable dilated convolution to extract more feature information for fiber classification. We also introduce a new activation function that enhances the nonlinear representation of the model and allows it to more fully extract negative feature information. Experimental results demonstrate that CWCNet achieves an accuracy rate of up to 98.438% on our self-built dataset, which is a 2.084% improvement over the original ShuffleNetV2 model. Furthermore, our proposed method outperforms classical models such as EfficientNetB0, MobileNetV2, Wide-ResNet50, and ShuffleNetV2 in terms of recognition accuracy while remaining lightweight.The method is capable of extracting more information on fiber characteristics and has the potential to replace manual labor as technological advancements continue to be made. This will greatly benefit engineering applications in the textile industry by providing more efficient and accurate fiber classification.

Developing high performance in titanium carbide nanocomposites containing hybrid SiC nanowire and CNT

Lightweight titanium carbide (TiC) has garnered considerable engineering applications in various advanced manufacturing industries. However, the intrinsic brittleness of TiC significantly limited its further applications. High-toughness and damage-tolerance TiC is always highly desirable for industry. Herein, we developed high-performance TiC nanocomposites reinforced by hybrid carbon nanotube (CNT) and SiC nanowire (SiCnw) through two-step spark plasma sintering, highlighting the synergic role of CNT and SiCnw on the microstructure and properties of the TiC matrix. Specifically, CNT was helpful in maintaining the high aspect ratio of SiCnw during the sintering process, while SiCnw contributed to the homogeneous distribution of CNT throughout the TiC matrix. The flexural strength and fracture toughness were simultaneously enhanced by 48.1% and 56.9% in case of CNT/SiCnw ratio of 1:2 comparing with pure TiC, respectively. This study provided the new insight on developing high-performance TiC materials.

Performance of Bayesian EWMA control chart with measurement error under ranked set sampling schemes with application in industrial engineering

The objective of this study is to investigate the behavior of the Bayesian exponentially weighted moving average (EWMA) control chart in the presence of measurement error (ME). It explores the impact of different ranked set sampling designs and loss functions on the performance of the control chart when ME is present. The analysis incorporates a covariate model, multiple measurement methods, and a conjugate prior to account for ME. The performance evaluation of the proposed Bayesian EWMA control chart with ME includes metrics such as average run length and standard deviation of run lengths. The findings, obtained through Monte Carlo simulation and real data application, indicated that ME significantly affects the performance of the Bayesian EWMA control chart when RSS schemes are employed. Particularly noteworthy is the superior performance of the median RSS scheme compared to the other two schemes in the presence of ME.

Hybrid flow-shop scheduling in collaborative manufacturing with a multi-crossover-operator genetic algorithm

Collaborative manufacturing systems have become a key component of Industry 4.0, supported by Industrial Information Integration Engineering (IIIE) applications. Efficient scheduling and coordination of manufacturing tasks are vital for these systems, directly impacting factory operational efficiency. One of the most prominent optimization problems in collaborative manufacturing is the hybrid flow shop scheduling problem with multiprocessor task (HFSPMT). This study proposes an improved genetic algorithm with multi-crossover-operator, called MCO-GA. MCO-GA introduces a novel crossover operator named SX, which demonstrates superior convergence efficiency compared to classical crossover operators. Furthermore,MCO-GA utilizes a probability selection method to autonomously choose between classical crossover operators and SX during the crossover stage. The effectiveness of MCO-GA is demonstrated through its successful application in solving a scheduling problem in a real-life wood manufacturing factory. Comparing MCO-GA with the state-of-the-art metaheuristic algorithms (HSA, OBL_HSA, and MGLS), it is observed that MCO-GA achieves significantly better average results in over 60% of instances. Additionally, MCO-GA outperforms OBL_HSA and MGLS in terms of computation time. These results highlight the effectiveness and efficiency of MCO-GA in solving the HFSPMT problem and its potential for improving scheduling and coordination in collaborative manufacturing systems.

A Hybrid Health Monitoring Approach for Aircraft Flight Control Systems With System-Level Degradation

This paper proposes a novel hybrid health monitoring approach to monitor the system-level degradation of aircraft flight control systems (FCSs). The idea derives from observing the nonlinear hysteresis phenomenon in FCS. First, a health FCS model is developed to implement the nonlinear degradation signal extraction of FCS by building an adaptive-network-based fuzzy inference system, a data-driven method. Subsequently, the Jump Markov autoregressive exogenous (JMARX) system with time delays is adopted to establish the FCS system-level degradation model. An expectation maximum-convex optimization algorithm is innovatively proposed to identify the model parameters. After that, three health indicators associated with the degradation model parameters are utilized for FCS system-level health monitoring. Finally, a practical flight experiment is conducted by a civil aircraft. The obtained experimental data is used to validate the effectiveness of the proposed FCS monitoring approach. The model of the time-delay JMARX system gets good modeling evaluation results on mean absolute error, standard deviation, etc. Besides, each of the three health indicators shows a clear FCS degradation tendency, which indicates that the proposed method successfully extracts the degradation information and monitors the health states of FCS. This approach is potential for practical and effective engineering applications in the aviation industry.

Preface

The 2nd International Conference on Advances in Modern Physics Sciences and Engineering Technology - PSET 2023 has been held on April 29th-30th, 2023, in Wuhan, China. This conference is organized by the China University of Geosciences (Wuhan), China, and the Istituto Nazionale di Fisica Nucleare (INFN), in collaboration with Wuhan University, China, University of Huddersfield, UK, University of Messina, Italy, and University of Ferrara, Italy.The primary goal of this conference is to provide a platform for presenting the latest advancements and research findings in engineering technology and applied physics from around the world. We aspire to foster research opportunities and collaborations that will help to advance the fields of modern physics sciences and technologies, particularly for young scientists and engineers.This volume of proceedings comprises comprehensive research papers, experience reports, and empirical studies contributed by scholars from around the world. In total, 75 papers submitted to PSET2023 and 50 papers underwent a meticulous peer-review process, with each paper being evaluated by at least one expert in the relevant field. Following rigorous assessment for quality and originality, finally, 31 papers were chosen for inclusion in this volume for publication. These papers include research and development in various areas such as mechanical and materials science, industrial engineering technology and applications, simulation, modelling and optimization technologies. The conference showcased numerous informative technical papers, including groundbreaking research presented by participants from various institutions. It was a platform for state-of-the-art research, lively discussions, and friendly exchanges.We extend our heartfelt gratitude to all keynote speakers: Prof. Dr. Osman Adiguzel, Firat University, Turkey, Prof. Alexander G. Ramm, Kansas State University, USA and Prof. Shuifa Shen, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, China. Thank the international reviewers and program committee members for their valuable contributions and dedication in making this conference a success.We look forward to meeting you next year!Sergei KopeikinUniversity of Missouri, USARoberto ZivieriNational Institute of High Mathematics (INdAM), Rome, ItalyList of Internationals Committee Members are available in the pdf

A soft sensor model based on CNN-BiLSTM and IHHO algorithm for Tennessee Eastman process

Accurate measurement of key variables plays an important role in on-line monitoring and control optimization of industrial processes. Soft sensor provides an effective method for measuring key variables. Deep learning model has strong learning ability and non-linear mapping ability for high-dimensional data. Many scholars have applied it to soft sensor, but there are many parameters, so it‘s necessary to optimize the parameters. In addition, how to select appropriate features for soft sensor modeling is also a key issue. The selection of appropriate variables can help to improve the performance of soft sensor model. Therefore, this study proposed a soft sensor model based on convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) and improved Harris hawk optimization (IHHO) algorithm for Tennessee Eastman (TE) process. First, Random forest (RF) algorithm is used to select auxiliary variables. Secondly, the Circle mapping initialization and the search strategy of simulated annealing (SA) are introduced to improve performance of HHO algorithm. Finally, the CNN-BiLSTM model is constructed for soft sensor modeling of key variables for the TE process. The simulation results prove that the proposed soft sensor model has good performance and high accuracy, can meet practical application of industrial engineering.

Reliability analysis of the multi-state system with nonlinear degradation model under Markov environment

Research conducted on multi-state systems has garnered significant attention as a result of their engineering applications in the aerospace industry. The paper explores the intricacies of multi-state systems operating in complex environments and experiencing degeneration processes. A mixed shock model is introduced, which consists of multiple run shock models and extreme shock models. A state transition matrix is constructed using a Markov chain embedding method to establish a reliability model of hard failure process. In contrast to the traditional reliability modeling approach of hard failure process, this method incorporates time-dependencies. A numerical integration method is employed to derive the reliability function of the soft failure process based on the Gamma process. To validate the effectiveness of the developed methodology, an application of a micro-engine is utilized. Finally, compared with the Monte Carlo simulation, the developed model can be considered as a useful tool for the system manager to assess the reliability of the multi-state system with the nonlinear degradation process.

Logistics-involved task scheduling in cloud manufacturing with offline deep reinforcement learning

As an application of industrial information integration engineering (IIIE) in manufacturing, cloud manufacturing (CMfg) integrates enterprises’ manufacturing information and provides an open and sharing platform for processing manufacturing tasks with distributed manufacturing services. Assigning tasks to manufacturing enterprises in the CMfg platform calls for effective scheduling algorithms. In recent years, deep reinforcement learning (DRL) has been widely applied to tackle cloud manufacturing scheduling problems (CMfg-SPs) because of its high generalization and fast-responding capability. However, the current DRL algorithms need to be trial-and-error through online interaction with the environment, which is costly and not allowed in the real CMfg platform. This paper proposes a novel offline DRL scheduling algorithm that alleviates the online trial-and-error issue while retaining DRL’s original advantages. First, we describe the system model of CMfg-SPs and propose the sequential Markov decision process modeling strategy, where all tasks are regarded as one agent. Then, we introduce the framework of the decision transformer (DT), which converts the online scheduling decision-making problem into an offline classification problem. Finally, we construct an attention-based model as the agent’s policy and train it offline under the DT architecture. Experimental results indicate that the proposed method consistently matches or exceeds online DRL algorithms, including deep double q-network (DDQN), deep recurrent q-network (DRQN), proximal policy optimization (PPO), and the offline learning algorithm behavior cloning (BC) in terms of scheduling performance and model generalization.

Smart Manufacturing: Latest Technologies And Applications In Industrial Engineering

Penelitian ini mengkaji tentang kajian tentang teknologi terbaru smart manufacturing dan aplikasinya dalam teknik industry.. Penelitian yang digunakan oleh peneliti adalah Sistematic Literature Review dengan data yang diperoleh dari jurnal dan artikel penelitian dari tahun 2019-2023. Platform sumber data yang digunakan pada penelitian ini Google Scholar dan Sage Jounal. Hasil dari penelitian ini menunjukkan bahwasannya Smart Manufacturing (SM) atau Indusry 4.0 adalah istilah yang umumnya diterapkan pada peningkatan operasi manufaktur melalui integrasi sistem, menghubungkan kemampuan fisik dan siber, dan memanfaatkan informasi termasuk memanfaatkan evolusi data besar. Adopsi SM telah terjadi secara tidak merata di seluruh industri, sehingga ada peluang untuk melihat ke industri lain untuk menentukan solusi dan jalur peta jalan untuk industry. Keunggulan operasional setelah penerapan Industri 4.0 akan mencapai standar tingkat baru yang tidak terbayangkan dalam organisasi manufaktur tradisional karena penerapan manufaktur cerdas. Organisasi yang berkaitan dengan teknik industri akan berkontribusi pada pengurangan emisi lingkungan karena Industri 4.0 akan memantau tingkat emisi dan mengatur diri sendiri secara proaktif melalui sistem fisik siber untuk mengurangi dampak lingkungan. Bahaya dan aktivitas yang tidak aman secara ergonomis akan diotomatisasi dan oleh karena itu, kesehatan pekerja akan terjaga. Tidak hanya itu, implementasi dari Smart Manufacturing pada teknik industry akan berengaruh sgnifikan pada hasil dan proses produksi baik waktu, biaya hingga keberlanjutan.

Monitoring of Location Parameters with a Measurement Error under the Bayesian Approach Using Ranked-Based Sampling Designs with Applications in Industrial Engineering

To detect sustainable changes in the production processes, memory-type control charts are frequently utilized. This study is conducted to assess the performance of the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart using ranked set sampling schemes following two different loss functions in the presence of a measurement error for posterior and posterior predictive distributions using conjugate priors. This study is based on the covariate model and multiple measurement methods in the presence of a measurement error (ME). The performance of the proposed Bayesian-AEWMA control chart with ME has been evaluated through the average run length and the standard deviation of the run length. Finally, a real-life application in semiconductor manufacturing was conducted to evaluate the effectiveness of the proposed Bayesian-AEWMA control chart with a measurement error based on different ranked set sampling schemes. The results demonstrate that the proposed control chart, in the presence of a measurement error, performed well in detecting out-of-control signals compared to the existing control chart. However, the median ranked set sampling scheme (MRSS) proved to be better than the other two schemes in the presence of a measurement error.