Prevention and protection of explosions are two notions often used subjectively, and to transform them into operative terms of decision support, it is indispensable to develop quantitative or semiquantitative approaches to determine the hazardous zones. The “classical and point-source” approaches that determine ATEX (explosive atmospheres) zones are semiquantitative methods that can meet the requirements of the ATEX directives (Directives 99/92/EC and 94/9/EC). The methodology’s principle in determining ATEX zones consists in making a comparison with typical examples “classical approach” and to identify the source points, determine the degree of discharge, identify the type of the zone, determine the radius of the zone and ultimately the extent and shape of this zone “source point approach.” The aim of this work is, on the one hand, to propose and present a classification methodology of the ATEX zones and, on the other hand, to apply the proposed methodology in a hydrocarbon separator.

The coronavirus (COVID-19) pandemic poses an unprecedented global challenge, impacting profoundly on health and wellbeing, daily life, and the economy around the world. The COVID-19 pandemic has also changed education forever. The COVID-19 has resulted in schools shut all across the world. Globally, all children at schools or students at universities are out of the classroom. As a result, education has changed dramatically, with the notable rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. Batna 2 University -situated in East of Algeria- is one of the universities suggested after the spread of COVID-19 in March, that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay. All institutes and departments, including the Industrial Engineering department, are started using the e-learning Moodle platform to publish courses for all degrees of study and establish online sessions, especially for Ph.D. students.

The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

Purpose This paper aims to investigate an integrated approach that aims at enhancing the application process of value stream mapping (VSM) method. It also proposes an extended VSM called Economic and Environmental VSM(E-EVSM). The proposed approach highlights the improvement of economic and environmental performances. Design/methodology/approach The proposed approach has studied the integration of VSM, fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy quality function deployment (QFD) to improve the economic and environmental performances of manufacturing processes. The VSM method is used for data collection and manufacturing process assessment, whereas fuzzy DEMATEL is used to analyse the current state map. Finally, fuzzy QFD is used to organize the improvement phase of VSM method. Findings The clear findings of this research prove the effectiveness of VSM method on the environmental and economic performances of manufacturing processes. In addition, the proposed approach will show the advantages of fuzzy DEMATEL and fuzzy QFD approaches in improving the application of the VSM method. Research limitations/implications The limitation of this study includes the lack of consideration of other dimensions such as social, technological and managerial. In addition, the proposed approach studied an average set of environmental and economic indicators. Originality/value The novelty of the proposed approach is proved by the development of an extended VSM method (E-EVSM). Also, the proposed approach contributes by a new methodology for analysing and improving the current state map of manufacturing processes.

This paper presents a new hardware implementation of a supervision system used in robot manipulators with two degrees of freedom. In addition to the simulation results, the new System Generator tool of Xilinx r is used to ensure self-generation of HDL codes. This code is used to configure field programmable gate arrays (FPGA) devices in the loop, and the supervision system is used mainly to ensure real-time reconfiguration of robots. In the proposed system, we used a new fault detection (FD) method for a viscous friction fault in the supervised robot combined with a fault-tolerance control method. The first module, based on residual analysis, is used to FD and to properly estimate the necessary corrections of the second module. For data transmission between the supervisor and the supervised robots, we used an approach based on the transmission control protocol. The simulation results show that the proposed method adjusts the fault effect using information transferred from the remote supervisor robot. The hardware implementation generated using Xilinx r System Generator is used to validate the proposed contribution and to ensure real-time processing in the case of industrial robots. The simulation results and the response times of both proposed systems are compared and discussed.

The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.

The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

Communication and teamwork are among the most recurrent skills associated with knowledge of engineering sciences. However, their application is not simple, due to the lack of a pedagogical approach that contributes to the development of knowledge based on experience. The problem in factories is the lack of daily self learning to avoid the essential presence of the experts in to resolve problems. In this work, we defined what is a learning organization, what is a tutorial and why a personalized tutorial in a trade, its different forms and steps for the development of a tutorial. After we gave a presentation of the company that is Textile Batna. This article discusses how to design a personalized tutorial, oriented and aimed at learning and knowledge transfer in the industry. By developing this system we aim to build an experimental database serving to preserve the knowledge of the production industry expertise of the Batna textile factory. We have designed a tutorial for the company in the form of a website. For this, the UML language was used. The tutorial features were presented. It helped employees to aquire certain skills without assistance of experts.

Purpose The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes. Design/methodology/approach The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side. Findings The results carried out from this study help the managers’ staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production. Research limitations/implications The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated. Originality/value The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.

Manufacturing automation is a double-edged sword, on one hand, it increases productivity of production system, cost reduction, reliability, etc. However, on the other hand it increases the complexity of the system. This has led to the need of efficient solutions such as artificial techniques. Data and experiences are extracted from experts that usually rely on common sense when they solve problems. They also use vague and ambiguous terms. However, knowledge engineer would have difficulties providing a computer with the same level of understanding. To resolve this situation, this article proposed fuzzy logic to know how the authors can represent expert knowledge that uses fuzzy terms in supervising complex industrial processes as a first step. As a second step, adopting one of the powerful techniques of machine learning, which is Support Vector Machine (SVM), the authors want to classify data to determine state of the supervision system and learn how to supervise the process preserving habitual linguistic used by operators.

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

In this paper, we propose new fusion and selection approaches to accurately predict the remaining useful life. The fusion scheme is built upon the combination of outcomes delivered by an ensemble of Gaussian process regression models. Each regressor is characterized by its own covariance function and initial hyperparameters. In this context, we adopt the induced ordered weighted averaging as a fusion tool to achieve such combination. Two additional fusion techniques based on the simple averaging and the ordered weighted averaging operators besides a selection approach are implemented. The differences between adjacent elements of the raw data are used for training instead of the original values. Experimental results conducted on lithium-ion battery data report a significant improvement in the obtained results. This work may provide some insights regarding the development of efficient intelligent fusion alternatives for further prognostic advances.

This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.

This article showcases the adaptability of existing mobility devices for the Algerian disabled population. It aims to develop a behavior model of disabled Algerian persons through (1) development of a theoretical model based on literature review and (2) improvement of this model by using local collected data from our developed questionnaire.

Lifetime service of Reinforced Concrete (RC) structures is of major interest. It depends on the action of the superstructure and the response of soil contact at the same time. Therefore, it is necessary to consider the soil-structure interaction in the safety analysis of the RC structures to ensure reliable and economical design. In this paper, a finite element model of soil-structure interaction is developed. This model addresses the effect of long-term soil deformations on the structural safety of RC structures. It is also applied to real RC structures where soil-structure interaction is considered in the function of time. The modeling of the mechanical analysis of the soil-structure system is implemented as a one-dimensional model of a spring element to simulate a real case of RC continuous beams. The finite element method is used in this model to address the nonlinear time behavior of the soil and to calculate the consolidation settlement at the support-sections and the bending moment of RC structures girders. Numerical simulation tests with different loading services were performed on three types of soft soils with several compressibility parameters. This is done for homogeneous and heterogeneous soils. The finite element model of soil-structure interaction provides a practical approach to show and to quantify; (1) the importance of the variability of the compressibility parameters, and (2) the heterogeneity soil behavior in the safety RC structures assessment. It also shows a significant impact of soil-structure interaction, especially with nonlinear soil behavior versus the time on the design rules of redundant RC structures.

The behavior of granular materials during loading depends on the level of stresses. When confining pressure increases, the peak shear strength, the residual shear strength and the stiffness gradually decrease; besides, the volumetric behavior is shown to be influenced by the stress level. In this paper, such effects, due to changes in stress levels, have been incorporated into a modified von Wolffersdorff hypoplastic model. For this purpose, reference void ratios and exponent α and β, the parameters of the original hypoplastic model are modified using experimental data. The performance of the proposed model is demonstrated by using simulated triaxial tests on Hostun sand with cell pressures up to 15 MPa. The study shows the ability of the improved model to highlight the behavior characteristics of granular materials in dilatancy and (peak) resistance under high stress better than the original model.

Soil-structure interaction is the key to study the behavior of structures under static or dynamic loading. The pile foundation is adopted to transfer loads from the structure to the soil when the structure is embedded in a weak soil stratum. Soil-pile system has a nonlinear behavior; thus, it is more complicated to understand. This study focuses on the numerical investigation of interaction of soil–pile–structure system (ISPS) and interaction of soil–pile system (ISP) under lateral loads. Nonlinear static analysis is carried out considering the lateral capacity of ISPS and ISP systems under lateral loading using pushover analysis. A parametric study concerning different types of axial loading, pile length and pile radius, as well as longitudinal steel ratio in different types of sand is conducted to observe the response of (ISPS) and (ISP) systems. Besides that, lateral capacity deflection and moment curves, as well as the formation of plastic hinge are evaluated for ISPS and ISP systems for a typical pile and various soil types and their results are presented. The results show that the lateral capacity is influenced by the parametric study.