The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.

Research on risk matrices show that there is considerable diversity in the practice of designing risk matrices. This has led to serious problems of standardization and communication. Indeed, these problems affect at the same time on the development of matrices and in their exploitation in term of risk assessment. To solve these problems, this paper proposes an experience feedback method that aims to capitalize the feedback invariants resulting from the analysis of existing risk matrices. This capitalization allows developing a theoretical framework of the robust risk matrices design. The application of the proposed method for examples of matrices confirms the interest of articulating these risk matrices designs through an argument based on experience feedback. In this sense, the merit of the proposed experience feedback method is that it promotes the sharing of knowledge between the actors involved in a risk assessment.

The ageing of the Algerian oil and gas (O&G) installations has led to many incidents. Such installations are over 30 years old (life cycle) and still in operation. To deal with this O&G crucial problem, the Algerian authorities have launched a rehabilitation and modernization schedule of these installations. Within the framework of this program, many audit operations are initiated to elaborate a general diagnosis of the works to be performed while optimizing production. In other words, industrial ageing risks shall be controlled. In the process safety management (PSM) context, the aim of this paper is to study ageing problem of the Algerian industrial installations through proposed indicators. Their prioritization adjusted by (TOPSIS) Technique for Order-Preference by Similarity to Ideal Solution method which allows identification of ageing control solutions of Algerian onshore fields.

Purpose The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities. Design/methodology/approach A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model. Findings A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources. Practical implications A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality. Originality/value The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

Purpose The purpose of this paper is to achieve the national strategic agenda’s criteria that aim for accomplishing sustainable buildings by estimating the effects of energy efficiency measures in order to reduce energy consumption and CO2 emission. Design/methodology/approach A design approach has been developed based on simulation software and a modeled building. Therefore, a typical office building is considered for testing five efficiency measures in three climatic conditions in Algeria. This approach is conducted in two phases: first, the analysis of each measure’s effect is independently carried out in terms of cooling energy and heating energy intensities. Then, a combination of optimal measures for each climate zone is measured in terms of three sustainable indicators: final energy consumption, energy cost saving and CO2 emission. Findings The results reveal that a combination of optimal measures has a substantial impact on building energy saving and CO2 emission. This saving can rise to 41 and 31 percent in a hot and cold climate, respectively. Furthermore, it is concluded that obtaining higher building performance, different design alternatives should be adapted to the climate proprieties and the local construction materials must be applied. Originality/value This study is considered as an opportunity for achieving the national strategy, as it may contribute in improving office building performance and demonstrating a suitable tool to assist stakeholders in the decision making of most important parameters in the design stage for new or retrofit buildings.

Aiming to provide a generalized method for assessing the performance of safety instrumented systems with a flexible and accurate consideration of the common cause failures’ contribution. This paper is devoted to the development of a direct way to generate the transition rate matrix associated with the continuous-time Markov model of any typical KooN architecture using any parametric model. Such a choice is considered after a detailed comparison of the ability of several dependability methods (e.g., fault trees, reliability block diagrams, Markov models, Bayesian networks, etc) to provide simple representations and genuine results in this context. To validate the developed method, the unavailability and the unconditional failure intensity of a wide range of configurations are quantified using the Binomial Failure Rate model and compared to those of the complete fault tree implementation.

Purpose The purpose of this paper is to show the impact of operational and environmental conditions (risk influencing factors) on the component criticality of safety barriers, safety barrier performance and accidents frequency and therefore on risk levels. Design/methodology/approach The methodology focuses on the integration of criticality importance analysis in barrier and operational risk analysis method, abbreviated as BORA-CIA. First, the impact of risk influencing factors (RIFs) associated with basic events on safety barrier performance and accident frequency is studied, and then, a risk evaluation is performed. Finally, how unacceptable risks can be mitigated regarding risk criteria is analyzed. Findings In the proposed approach (BORA-CIA), the authors show how specific installation conditions influence risk levels and analyze the prioritization of components to improve safety barrier performance in oil and gas process. Practical implications The proposed methodology seems to be a powerful tool in risk decision. Ordering components of safety barriers taking into account RIFs allow maintenance strategies to be undertaken according to the real environment far from average data. Also, maintenance costs would be estimated adequately. Originality/value In this paper, an improved BORA method is developed by incorporating CIA. More precisely, the variability of criticality importance factors of components is used to analyze the prioritization of maintenance actions in an operational environment.

Purpose This paper aims to propose a process optimization approach showing how organizations are able to achieve sustainable and efficient process optimization, based on integrated process-risk analysis using several criteria to a better decision-making. Design/methodology/approach Several approaches are used (functional/dysfunctional) to analyze how processes work and how to deal with risks forming multi-criteria decision-making. In addition, a risk factor is integrated into the structured analysis and design techniques (SADT) method forming a novel graphical view SADT-RISK; it identifies process’s failures using the traditional failure modes, effects and criticality analysis (FMECA) and economic consideration “failure mode and effect, criticality analysis-cost FMECA-C” making a multi-criterion matrix for better decision-making. Subsequently, some recommendations are proposed to overcome the failure. Findings This paper illustrates a methodology with a case study in a company, which has a leading brand in the market in Algeria. The authors are integrating a varied portfolio of approaches linking with each other to analyze, improve and optimize the processes in terms of reliability and safety to deal with risks; reduce the complexity of the systems; increase the performance; and achieve a safer process. However, the proposed method can be readily used in practice. Originality/value The paper provides a new approach based on integrated management using new elements as an innovative contribution, forming a novel graphical view SADT-RISK; it identifies process’s failures using the traditional FMECA and economic consideration “a new multi-criterion matrix for better decision-making and using the SWOT analysis – Strengths, Weaknesses, Opportunities, Threats – as a balance to decide about the process improvement”. The authors conclude that this methodology is oriented and applicable to different types of companies such as financial, health and industrial as illustrated by this case study.

Renewable power generation facilities are constantly expanding due to the expected depletion of fossil fuels and the increasingly demanding policy of pollution control. Having said that, hydrogen is one of the promising energy sources. That said, hydrogen chain safety is an unescapable parameter that should continuously coexist with the development of hydrogen domain. In this context, this article presents a contribution to the risk analysis and evaluation of a complex hydrogen production system ’EGA-9000' at CIEMAT (Centre for Research on Energy, Environment and Technology - Madrid, Spain). The methodology followed in this study revolves around the risk analysis process through a FAST (Functional Analysis System Technique) functional analysis method and a HAZOP (HAZard and Operability) dysfunctional analysis method. The evaluation of the thirty-three scenarios identified by the risk analysis shows that the studied system is insecure. Indeed, five scenarios at an unacceptable level of risk. And it is noted that the risk of fire and explosion is the major risk for all scenarios studied. To this end, safety measures (recommendation) have been proposed based on the weaknesses detected by the risk analysis carried out.

The industrial risk mapping is a topical problem in the field of risk management that attracts many researchers to develop risk matrices to ensure consultation between their actors. In this context, this paper aims to propose the principal component analysis (PCA) method as support for this consultation. Indeed, the use of PCA method is justified by its robustness for aggregate initial data associated with industrial risks as principal factors and ranking of this risk in terms of their criticalities in risk matrices. However, the aggregation of initial data on industrial risks by the main factors, in some cases, leads to inaccuracies which make it difficult to classify certain risks. This paper proposes two variants of PCA method to solve this inaccuracy and succeeds in classifying risks according to their respective criticalities, namely PCA-Improved (PCA-I) and PCA-I-Fuzzy (PCA-IF). The results come from the PCA application and its proposed variants (PCA-I and PCA-IF) on an example of accident scenarios ranking. We have established a scientific basis for the capitalization of mapping tool for consultation and decision support to industrial risk managers.

This paper presents a hybrid scheme for the control of active and reactive powers using the direct vector control with stator flux orientation (SFO) of the DFIG. The hybrid scheme consists of Fuzzy logic, Reference Signal Tracking (F-RST) controllers. The proposed (F-RST) controller is compared with the classical Proportional-Integral (PI) and the Polynomial (RST) based on the pole placement theory. The various strategies are analyzed and compared in terms of tracking, robustness, and sensitivity to the speed variation. Simulations are done using MATLAB software. The simulation results prove that the proposed approach leads to good performances such as the tracking test, the rejection of disturbances and the robustness concerning the parameter variations. The hybrid controller is much more efficient compared to those of PI and RST controller, it also improves the performance of the powers and ensures some important strength despite the parameter variation of the DFIG.

This paper presents a hybrid scheme for the control of active and reactive powers using the direct vector control with stator flux orientation (SFO) of the DFIG. The hybrid scheme consists of Fuzzy logic, Reference Signal Tracking (F-RST) controllers. The proposed (F-RST) controller is compared with the classical Proportional-Integral (PI) and the Polynomial (RST) based on the pole placement theory. The various strategies are analyzed and compared in terms of tracking, robustness, and sensitivity to the speed variation. Simulations are done using MATLAB software. The simulation results prove that the proposed approach leads to good performances such as the tracking test, the rejection of disturbances and the robustness concerning the parameter variations. The hybrid controller is much more efficient compared to those of PI and RST controller, it also improves the performance of the powers and ensures some important strength despite the parameter variation of the DFIG.

The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

Anti-lock Braking System (ABS) is used in automobiles to prevent slipping and locking of wheels after the brakes are applied. Its control is a rather complicated problem due to its strongly nonlinear and uncertain characteristics. The aim of this paper is to investigate the wheel slip control of the ground vehicle, comprising two new strategies. The first strategy is the Sliding Mode Controller (SMC) and the second one is the Fuzzy Sliding Mode Controller (FSMC), which is a combination of fuzzy logic and sliding mode, to ensure the stability of the closed-loop system and remove the chattering phenomenon introduced by classical sliding mode control. The obtained simulation results reveal the efficiency of the proposed technique for various initial road conditions.

Unified power flow controller is a power electronics-based device utilised to improve transmission line capacity and control power flow transmitted by power transmission systems. This paper presents the application of the decoupled control strategy to control independently active and reactive power in the event of changes in the step points of the powers. Two types of controllers are used to handle the control strategy proposed: conventional PI regulator and fuzzy logic PI regulator. The fuzzy logic controller must have high performance to handle the problem of adjustment of power decoupling. Also, a three level neutral point controller inverter is used in both series and shunt parts of the UPFC to get multistep voltage wave and improve the power quality.

The paper presents two fuzzy logic control algorithms: type-1 and type-2. These two nonlinear techniques are used for adjust the speed control with a direct stator flux orientation control of a doubly fed induction motor. The effectiveness of the proposed control strategy is evaluated under different operating conditions such as of reference speed and for load torque step changes at nominal parameters and in the presence of parameter variation (stator resistance, rotor resistance and moment of inertia). The results of the simulation of the doubly fed induction motor velocity control have shown that fuzzy type-2 ensures better dynamic performances with respect to fuzzy type-1 control, even by parametric variations and external disturbances.