Process Modeling Techniques Research Articles

Quantifying natural and anthropogenic impacts on streamflow and sediment load reduction in the upper to middle Yellow River Basin

Study RegionUpper to Middle Yellow River Basin (UMYRB). Study FocusClimate, land use, and landscape engineering measures are the main drivers affecting watershed hydrology, yet disentangling their respective contributions over large and complex regions is a great challenge. We combine process modeling techniques and hydrological observations to investigate the temporal changes of streamflow and sediment in the UMYRB during 1971–2016 and the imprints of climate change and anthropogenic activities. New hydrological insights for the regionLong-term statistical analysis shows that streamflow and sediment decreased over time across the basin with an increasing magnitude of reduction from upstream to midstream. Streamflow of UMYRB and sediment in the upstream area have decreased by 37.59 % and 71.86 %, respectively, since their change-point years. Analytical modeling results in the UMYRB demonstrate that 77.30 % of the streamflow reduction was attributed to landscape engineering measures, 16 % to climate change and the remaining 6.70 % to land use change. For sediment reduction, landscape engineering measures appeared to be the sole decisive factor for the upstream (over 126 %), while climate and land use changes positively affected sediment yield. Our study highlights the importance of considering the impacts of multiple factors when evaluating hydrological changes in large basins, and the method we adopted can be valuable elsewhere. Developing process-based methods to quantify the hydrological effects of engineering measures is still a research priority moving forward.

Solar-Assisted Carbon Capture Process Integrated with a Natural Gas Combined Cycle (NGCC) Power Plant—A Simulation-Based Study

In the realm of Natural Gas Combined Cycle (NGCC) power plants, it is crucial to prioritize the mitigation of CO2 emissions to ensure environmental sustainability. The integration of post-combustion carbon capture technologies plays a pivotal role in mitigating greenhouse gas emissions enhancing the NGCC’s environmental profile by minimizing its carbon footprint. This research paper presents a comprehensive investigation into the integration of solar thermal energy into the Besmaya Natural Gas Combined Cycle (NGCC) power plant, located in Baghdad, Iraq. Leveraging advanced process simulation and modeling techniques employing Aspen Plus software, the study aims to evaluate the performance and feasibility of augmenting the existing NGCC facility with solar assistance for post-carbon capture. The primary objective of this research is to conduct a thorough simulation of the Besmaya NGCC power plant under its current operational conditions, thereby establishing a baseline for subsequent analyses. Subsequently, a solar-assisted post-combustion capture (PCC) plant is simulated and seamlessly integrated into the existing power infrastructure. To accurately estimate solar thermal power potential at the Baghdad coordinates, the System Advisor Model (SAM) is employed. The integration of solar thermal energy into the NGCC power plant is meticulously examined, and the resulting hybrid system’s technical viability and performance metrics are rigorously evaluated. The paper contributes to the field by providing valuable insights into the technical feasibility and potential benefits of incorporating solar thermal energy into conventional natural gas power generation infrastructure, particularly in the context of the Besmaya NGCC plant in Baghdad. The power generation capacity of the plant was set at 750 MW. With this capacity, the annual CO2 generation was estimated at 2,119,318 tonnes/year which was reduced to 18,064 tonnes/year (a 99% reduction). The findings aim to inform future decisions in the pursuit of sustainable and efficient energy solutions, addressing both environmental concerns and energy security in the region.

Developing conceptual model for safety risk management in aviation as building capacity and resilience strategy

Purpose This study aims to develop an inclusive, multidisciplinary, flexible and organizationally adaptable safety risk management framework, including diversity management, that will be implemented to ensure safety is and remains at the desired level. If the number of incidents and potential incidents that could lead to accidents and their impact rates are to be reduced operationally and administratively, aviation safety risks and sources of risk must be better understood, sources of risk identified, and the safety risk management framework designed in an organization-specific and organization-wide sustainable way. At this point, it is necessary to draw the conceptual framework well and to define the boundaries of the concepts well. In this study, a framework model that can be adapted to the organization is proposed to optimize the management of risks and provide both efficient and effective resource allocation and organizational structure design in its operations and management functions. Design/methodology/approach The qualitative research method – triple techniques – was deemed appropriate for this study, which aims to identify, examine, interpret and develop the situations of safety management models. In this context, document analysis, business process modeling technique and Delphi techniques from qualitative research methods were used via integration as the methodology of this research. Findings To manage dynamic civil aviation management activities and business processes effectively and efficiently, the risk management process is the building block of the “Proposed Process Model” that supports the decision-making processes of aviation organizations and managers. This “Framework Conceptual Model” building block also helps build capacity and resilience by enabling continuous development, organizational learning, and flexible structuring. Research limitations/implications This research is limited to air transportation and aviation safety management issues. This research is limited specifically to a safety-based risk management framework for the aviation industry. This research may have social implications as source saving, optimum resource use and capacity building will make a contribution to society and add value besides operational and practical implementation. Social implications This research may contribute to more safe operations and functions in the aviation industry. Originality/value Management and academia may gain considerable support from this research to manage their safety risks via a corporate-tailored risk management framework, both improving resilience and developing corporate capacity. With this model presented, decision-makers will have a guiding structure that can optimally manage the main risk types that may be encountered in the safety risk in the fields of suppliers, manufacturers, demand changes, logistics, information management, environmental, legal and regulatory. Existing studies in the literature are generally in the form of algorithms and cannot be used as a decision-making support tool. This model aims to fill the gap in the literature. In addition, added value may be created by applying this model to optimum management safety risks in the real aviation industry and its related sectors.

BUSINESS PROCESS MODELLING PADA DIVISI PUBLIC RELATION PERGURUAN TINGGI

Public relations division is part of a company or organization that is responsible for preparing special communication plans with various media in an effort to build an image and good and beneficial relations between the organization and the public. Some of these public relations jobs are related to public communications such as managing incoming and outgoing mail. This research was conducted to design new business processes in the development of current business processes. Where the problem to be solved is a problem in terms of time. The business process flow will be described with the Business Process Modeling And Notation (BPMN) modeling technique. The research was carried out in several stages, namely data collection, information, literature study, analysis of the old process flow and business flow analysis of the recommendation process. Based on the research conducted, it was found that the new business process flow can speed up the processing time for the completion of incoming and outgoing letters. With the time analysis of this business process, the stages of work on the tasks of each section can be known in the form of time estimates that accelerate the business process of managing letters in the public relation division.

Eco-Economic Performance Estimation Method for Pretensioned Spun High-Strength Concrete Pile Installation

Pile installation has an environmental impact through its various processes ranging from raw material extraction to construction. In addition, the environmental performance, productivity, and cost of pile installation depend on the construction plan. Therefore, the chain of activities must be considered when analyzing the sustainability of pile installation. A rational construction plan must carefully examine the factors that affect the productivity and sustainability of pile installations. This study presents a method for evaluating eco-economic performance by analyzing the resource utilization and processes of PHC pile installation. First, a process modeling technique, wherein details are broken down to the work task level, based on energy consumption and resource cost, is proposed. Second, a simulation method that calculates the eco-economic performance of the PHC pile process and resources (e.g., equipment) is presented. Third, a quantitative comparison of durations, costs, and emissions resulting from simulation, estimation based on the CSPR (Construction Standard Production Rate) and IUC (Itemized Unit Cost), site contract, and actual construction is presented. The results reveal that the method effectively reflects the prediction of duration, cost, and carbon emissions generated in the real world during the construction planning stage.

Systematic Approach for Comparing Team-based Care Processes.

Team-based care process modeling techniques have focused on understanding and designing solutions for a single site. Less is known about tailoring an effective team-based care process from one site to another, which is necessary for multi-site implementation efforts. We propose an approach for analyzing and comparing a team-based care process performed at two sites to inform redesign opportunities. Our approach includes abstracting the goals and strategies of each process by identifying whether sociotechnical system element differences exist. Element differences may exist for the phase, tasks, roles, information, and technology and tools. Differences in system elements may still support process goals and strategies and, thus, be irrelevant for redesign opportunities. We demonstrate the utility of the approach using an operating room to intensive care unit handoff protocol. This approach should be useful for researchers and practitioners that are tailoring and implementing a successful team-based care process at more than one site.

Prediction of mechanical properties of In1-x GaxAsyP1-y lattice-matched to different substrates using artificial neural network (ANN)

ABSTRACT The mechanical properties, namely the elastic constants (C11, C12, and C44), bulk B, shear Cs, and Young’s modulus Y0, of the In1-xGaxAsyP1-y lattice-matched GaAs and InP substrates, were estimated using an artificial neural network (ANN method). This study aimed to create ANN networks that facilitate the estimation of the mechanical properties of alloys subjected to different temperatures and hydrostatic pressures. We aimed to predict the mechanical properties of InGaAsP alloys matched to InP and GaAs substrates as a function of temperature and hydrostatic pressure. ANN learning was performed based on the results validated by the empirical pseudopotential method data within the virtual crystal approximation, including the actual disorder potential. An ANN is a nonlinear process modeling technique frequently encountered in materials science. The predictive curves show high reliability, and underline the importance of using this approach to replace the time-consuming and costly experimental test, as well as to predict the mechanical response of the material where conventional modeling approaches have failed. Our method can consolidate the theoretical references found in the literature, it can also serve the field of engineering the mechanical properties of semiconductors, facilitate the successful design of optoelectronic devices, and serve future experimental works.

An Integrated Fuzzy-Based Sustainability Framework for Post-Secondary Educational Buildings: A User-Perspective Approach

Decreasing the impact of educational buildings on the environment is a primary concern of the sustainable building movement. However, limited research has centered on improving educational building sustainability from users’ perspectives. This study aims to determine an overall user perspective satisfaction scale (OUPS) for post-secondary educational buildings, determine the factors and sub-factors that affect users’ perspectives, and create a building sustainability platform for evaluating user satisfaction. The main goal of the developed model is to offer facility managers the current users’ perspectives on post-secondary educational buildings to improve sustainability from users’ perspectives. The results revealed a significant influence of thermal comfort (40%) and aesthetics (22%) on users’ perspectives. The developed model was validated by experts working in the facility management field and they acknowledged it as having good potential. This work first utilized the Fuzzy Analytic Network Process (FANP) modelling technique to determine weights for each factor and sub-factor and then employed the fuzzy expert system to develop an OUPS. Questionnaire responses and interviews with facility managers and users were used to develop these models for a case study of the M.B. building at Concordia University in Montreal, Quebec.

A multi-output multi-fidelity Gaussian process model for non-hierarchical low-fidelity data fusion

Multi-fidelity (MF) surrogate model has been widely used in dealing with computationally expensive problems as it can make a trade-off between computational cost and modeling accuracy. Most existing MF models are under the assumption of a hierarchical relationship between different fidelity models. In other words, the fidelity level of each low-fidelity (LF) model can be clearly identified. However, the fidelity rankings of the LF models are indistinguishable in many practical problems. Hence, the existing MF models may be inapplicable when dealing with multiple non-hierarchical LF data. To address this issue, a multi-fidelity Gaussian process model for multiple non-hierarchical LF data fusion is developed in multi-output scenarios. It focuses on approximating multiple dependent high-fidelity (HF) responses assisted by multiple non-hierarchical LF data. The multi-output Gaussian process modeling technique makes the proposed model able to catch the latent correlations between outputs. In such a structure, both the information of the multiple LF data and the correlations between outputs can be fully exploited to further enhance the prediction accuracy. The effectiveness of the proposed model is tested using two analytical examples and a metamaterial vibration isolator design problem. It is shown that the proposed model gives the best predictions among the tested models for most cases. Results show that the proposed method can be a potential multi-output multi-fidelity modeling approach for computationally expensive problems in dealing with multiple non-hierarchical LF data.

Environmental sustenance via melon seed peel conversion to fermentable sugars using soft computing models

There is a shift in focus on agro-wastes constituting menace through their disposal in the environment by converting them to value-added products within the sustainability framework. This study aimed at predicting melon seed peel (MSP) hydrolysis parameters using soft computing models such as Artificial Neural Network (ANN) and Adaptive neuro-fuzzy inference system (ANFIS). The capability of the models was evaluated using the correlation coefficient (R 2 ), mean square error (MSE), mean absolute deviation (MAD) and average absolute relative error (AARE). The experiments were conducted at an acid concentration (2.5–3.5 M), time (1–3 h), and temperature (80–100 °C) with output as sugar yield. The characterization results from the proximate analysis, FTIR, SEM, EDX, XRF and XRD indicated that MSP contains a significant amount of cellulose for fermentable sugar production. The sugar yield estimation performance indicators are: ANN (R 2 = 0.999, MSE = 1.2698E-12, MAD = 1.2%, AARE = 0.00231) and ANFIS (R 2 = 0.9782, MSE = 0.0334, MAD = 2.7%, AARE = 0.0054), these suggested a good agreement between the experimental and predicted values. The results showed that the ANN and ANFIS models effectively predict accuracy. It was concluded that soft computing models could be considered a viable chemical process modelling and simulation techniques.

Predicting remaining execution time of business process instances via auto-encoded transition system

As an important task in business process management, remaining time prediction for business process instances has attracted extensive attentions. However, most of the traditional remaining time prediction approaches only take into account formal process models and cannot handle large-scale event logs in an effective manner. Although machine learning and deep learning have been recently applied to the remaining time prediction task, these approaches cannot incorporate domain knowledge naturally. To overcome these weaknesses of existing studies, we propose a remaining execution time prediction approach based on a novel auto-encoded transition system, which can enhance the complementarity of process modeling and deep learning techniques. Through auto-encoding the event-level and state-level features, the proposed approach can represent process instances in a comprehensive and compact form. Furthermore, a transfer learning strategy is proposed to train the remaining time prediction model so as to avoid overfitting and improve the accuracy of prediction. We conduct extensive experiments on four real-world datasets to verify the effectiveness of the proposed approach. The results show its superiority over several state-of-the-art approaches.

Stochastic multi-fidelity surrogate modeling of dendritic crystal growth

In this work, we propose a novel framework coupling state-of-the-art multi-fidelity Gaussian Process modeling techniques with input-space warping for a cost-efficient construction of a stochastic surrogate model. During model generation, we achieve high computational efficiency by combining a large number of cheap estimates (low-fidelity model) with only a few, computationally expensive, high-fidelity measurements. We base the fidelity hierarchy on coarse-grid approximations of high-fidelity numerical simulations and show its successful application within the proposed framework. Utilizing coarse-grid approximations for multi-fidelity modeling is attractive for many practical applications, since it often allows for multi-fidelity data generation with a single simulator. As benchmark, we apply this framework to generate a surrogate model for crystal growth velocities in directional dendritic solidification. The derivation of a relation between this tip velocity and process parameters, such as undercooling, thermal diffusivity, capillarity, and capillary anisotropy, has been in the focus of research for decades due to its important role on microstructure evolution during solidification. It defines the thermo-mechanical properties of the solidified part and influences its behavior in subsequent manufacturing steps.As data generator, we use the open-source simulation framework ALPACA, applying a conservative sharp-interface level-set model. We assess the accuracy of the multi-fidelity tip velocity model by using cross-validation techniques. Compared to single-fidelity models purely based on high-fidelity data, our approach improves prediction accuracy significantly but only requires a little cost overhead for data generation. The stochastic nature of the resulting surrogate model allows for quantifying the uncertainty associated with predictions. This motivates the application of the model in Bayesian-optimization algorithms for inverse problems. Also, it may serve as input for microstructure simulations which rely on accurate relations between local solidification velocities and process parameters such as undercooling to predict grain-scale crystalline structures and which need material-dependent model calibration.

Process analytical technology as key‐enabler for digital twins in continuous biomanufacturing

AbstractOver the last few years rapid progress has been made in adopting well‐known process modeling techniques from chemicals to biologics manufacturing. The main challenge has been analytical methods as engineers need quantitative data for their workflow. Industrialization 4.0, Internet of Things, artificial intelligence and machine learning activities up to big data analysis have taken their share in solving fundamental problems like component‐ or at least group‐specific evaluation of spectroscopic data. Besides, concerning inline analytics methods included in process analytical technology concepts the key technology has been the generation of decisive validated digital twins based on process models. This review aims to summarize the methodology to achieve a holistic understanding of process models, control and optimization by means of digital twins using the example of recent work published in this field. © 2021 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

A novel BIM-based process workflow for building retrofit

The European Union (EU) has developed policies aimed at accelerating the retrofit of existing buildings, with the vision of a decarbonised building stock by 2050. Retrofitting of existing buildings offers significant opportunities to reduce global energy consumption and greenhouse gas emissions. Although there is a wide range of government incentives available, retrofits only affect 0.5–1% of the building stock per year. Thus, this research conducted a literature review in order to identify the barriers preventing retrofitting of existing buildings. These barriers include low financial availability, user awareness, uncertainty in regulatory frameworks and fragmentation of the supply chain. The research presented here overcomes these retrofit barriers by developing a novel step-by-step guideline for building retrofit. The proposed workflow combines the benefits of Building Information Modelling (BIM) with the Business Process Modelling (BPM) technique. BIM is used for its capacity to create and manage information along the full life-cycle of a building. While BPM allows to optimise the retrofit workflow, by providing all relevant stakeholders with (i) the instruments to better understand their roles and responsibilities in the retrofit process, (ii) the technological framework and data needed to efficiently implement BIM within the project. This novel BIM- BPM workflow was demonstrated on a residential pilot building located in Inis Mór, Ireland.

Adaptive Data Dimensionality Reduction for Chemical Process Modeling Based on the Information Criterion Related to Data Association and Redundancy

Chemical process modeling is the basis for research and applications in related fields. With the development of industrial informatization, data-driven process modeling techniques are increasingly applied in chemical processes, helping to obtain more accurate results with less model development costs. However, due to the high-dimensional nonlinear characteristics of most chemical processes, problems such as the “curse of dimensionality” and information redundancy will render the models more prone to overfitting with reduced accuracies and weaker generalization abilities. Many data dimensionality reduction methods are adopted to mitigate the above problems, but most are limited by inaccurate association measurements and weak redundancy exclusion. In this paper, the extensive existence of data associations and information redundancies is first revealed through the analysis from an information-theoretic perspective. Then, a feature selection method based on conditional refined maximal information coefficient maximization (CRMICM) is proposed to improve the consistency of association measurement and the accuracy of redundancy estimation with limited samples. The final prediction modeling test for an actual fluidized catalytic cracking (FCC) process proves the extensive association between the variables and targets. Only a few variables are essential for the modeling, while the rest are redundant. Compared with other methods, CRMICM achieves the best dimensionality reduction effects on the FCC process data regarding the number of features and model prediction accuracy, showing its good applicability for chemical processes.

Routines as a conceptual tool for studying resources management in SMEs: evidence from Brazilian bakeries

Purpose This study aims to investigate routines as a conceptual tool to analyze resources management in small and medium-sized enterprises’ (SMEs) productive processes. Design/methodology/approach The authors developed a qualitative multiple case study with Brazilian companies in the bakery industry. Data were collected through interviews, on-site observation and documentary analysis. Plus, the authors used business process modeling (BPM) techniques to map the observed routines. Findings The restrictions of SMEs accentuate the improvisation of routines. However, contrary to expected, many of these deviations expand the possibilities of organizational action as they become successful in terms of operational efficiency, which allows these companies to extract performance from ordinary resources and imitable management practices. Practical implications The BPM shows its value to track the allocation of resources in SMEs by recording the evolution of its routines and helping to preserve an operational memory. This finding could be useful to help public agencies to develop accessible management tools to assist small business owners. Originality/value Most of the conceptual tools developed to analyze the resources management are based on the study of large organizations, which may limit the analysis and lead to restricted or mistaken results if used in another context without proper adaptation. The authors apply an objective and representational epistemological lens to organizational routines to adapt it to the pragmatic context of operations management. Also, the authors suggest that better than a resource-based view, the practice-based view is a theoretical approach more compatible with the resource constraints context of SMEs.

Robots and the origin of their labour-saving impact

This paper investigates the presence of explicit labour-saving heuristics within robotic patents. It analyses innovative actors engaged in robotic technology and their economic environment (identity, location, industry), and identifies the technological fields more exposed to labour-saving innovations. It exploits advanced natural language processing and probabilistic topic modelling techniques applied to the universe of USPTO patent applications between 2009 and 2018, matched with the ORBIS (Bureau van Dijk) firm-level dataset. The results show that labour-saving patent holders comprise not only robot producers, but mainly adopters. Consequently, labour-saving robotic patents appear along the entire supply chain. Additionally, labour-saving innovations are directed towards manual activities in services (e.g. in the logistics sector), activities entailing social intelligence (e.g. in the healthcare sector) and cognitive skills (e.g. learning and predicting).

Mineralisation potential assessment using analytical hierarchy process (AHP) modeling technique: A case study of Ilesha schist belt, southwestern Nigeria

A mineralisation potential assessment in part of Ilesha Schist belt of southwestern Nigeria was carried out using multi criteria decision analysis (MCDA) in the context of analytical hierarchy process (AHP). Three parameters sourced from aeromagnetic, electrical resistivity and geological data were identified to favour gold mineralisation in the study area. These parameters include lineament density (Ld), lithology (Lt), and electrically derived coefficient of anisotropy (An). The application of data enhancement techniques such as total horizontal derivative, 3-D Euler deconvolution and source edge detection were employed in the production of lineament map of the study area. Two hundred and sixty-five (265) vertical electrical sounding (VES) points were investigated using Schlumberger configuration. Coefficient of anisotropy derived from the Dar-Zarouk parameters was obtained from the synthesized VES data. The lithologic information was obtained from the existing geological map of the area. The orientations of lineaments displayed on the aeromagnetic results suggest predominantly NE-SW trending structures, which are characteristic of the Pan African orogenic events in the area. However, few of the E-W trending lineaments are imprints of secondary structural alteration. Furthermore, the Euler deconvolution solutions revealed that the area is characterised by geological contacts with estimated depth of 94–600 m. The coefficient of anisotropy results obtained from the geo-electric parameters vary from 1.00–2.87. AHP weighting and ranking technique was used to develop a reliable mineralisation potential model (MPM). In line with the recommended lower limit of 10 %, the consistency ratio of the developed model is 9.61 %. The produced MPM was classified into low, moderate and high mineralisation potential zones. Preliminary validation of the MPM using the existing pits in the area showed 79 % success rate. The validation of the model using the existing geochemical information from the previous studies indicated 85 % agreement with the conceptual model; thus, confirming the reliability of the produced MPM. The developed model is therefore believed to serve as a future reference in the resource management of the study area.

Post-combustion carbon capture for tank to propeller via process modeling and simulation

The International Maritime Organization (IMO) has issued a long-term sustainable strategy to gradually phase out the major ship-related sources of greenhouse gas emissions. While the post-combustion carbon capture has been well investigated for onshore plants, the maritime carbon capture system is still in an early stage due to its unique characteristics and design limitations. Aiming at providing a holistic way to find the most efficient and sustainable PCC solution for an LNG tanker, this work proposed a system of the tank to propeller post-combustion carbon capture which integrates ship engine process modeling with chemical absorption/desorption process modeling techniques. A rate-based aqueous monoethanolamine (MEA) process model was developed and validated, then scaled up and modified to capture CO2 from the flue gas. To increase the carbon capture efficiency and decrease the energy penalty, this work provided detailed steps for process model development covering innovative absorber/stripper design under variation of solvents, packed type, and liquid to gas ratio. This work also included a thorough sustainability evaluation based on emission reduction efficiency, energy penalty, and carbon cyclic capacity among two single aqueous amines, MEA and diisopropanolamine (DIPA), and one blended amine with a promoter, methyldiethanolamine (MDEA) with piperazine (PZ). Although DIPA presented a better performance on energy requirement for solvent regeneration, the blended amine was the optimal solvent to meet the requirement of the IMO carbon abatement strategy. Compared to the seven molality MEA, the MDEA-PZ option could capture more than 57 % CO2 while around 25 % specific reboiler duty can be saved.

Modeling of textile manufacturing processes using intelligent techniques: a review

As the need for quickly exploring a textile manufacturing process is increasingly costly along with the complexity in the process. The development of manufacturing process modeling has attracted growing attention from the textile industry. More and more researchers shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of this paper is in line with the procedure of textile processes from yarn to fabrics, and then to garments. The analysis and discussion of the previous studies are conducted on different applications in different processes. The factors and performance properties considered in process modeling are collected in comparison. In terms of inputs’ relative importance, feature selection, modeling techniques, data distribution, and performance estimations, the considerations of the previous studies are analyzed and summarized. It is also concluded the limitations, challenges, and future perspectives in this issue on the basis of the summaries of more than 130 related articles from the point of views of textile engineering and artificial intelligence.