Various facilities are used in mineral processing to prepare raw material. Practically, two types of balls are used, cast balls and forged balls. They are respectively made from high chromium cast iron and forged steel and are supplied in different sizes and chemical compositions. The cast and forged balls have different microstructures and consequently display dissimilar wear behavior. The target aimed in this work is to achieve a comparative study taking into account the type of microstructure, mechanical properties, and wear behavior of these two kinds of materials. Specimens have undergone chemical, metallographic and XRD characterizations. Subsequently, these samples were subjected to hardness measurements, abrasion and friction tests in order to evaluate their wear behaviour. Tribological tests, under unlubricated environment, are carried out on both types of grinding balls in order to study the wear system. Corrosion tests are also performed on forged steel and high chromium cast iron ball samples. The obtained results reveal a large difference in terms of chemical composition and microstructural components. Chromium cast iron balls are more resistant to friction, whereas forged balls are more resistant to abrasion. Additionally, the corrosion tests reveal a narrow discrepancy in corrosion behaviour between the studied materials.

Purpose

The objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO₂ generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO₂) emitted by an industrial fire in a plant located in an industrial region site in Algeria.

Design/methodology/approach

The SA was defined for the following input parameters: wind speed, NO₂ emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.

Findings

The study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO₂ initial concentration has the most influence on the predicted NO₂ levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO₂ concentration estimation.

Originality/value

The study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

This paper presents a decision-making support system for situation risk assessment associated with critical alarms conditions in a gas facility. The system provides a human operator with advice on the confirmation and classification of occurred alarm. The input of the system comprises uncertain and incomplete information. In the light of uncertain and incomplete information, different uncertainties laws have been associated with the probabilistic assessment of the system loops which combine data of several sources to reach the ultimate classification. The implemented model used Observe-Orient-Decide-Act loop (OODA) combined with Bayesian networks. Results show that the system can classify the alarms system.

Purpose

The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.

Design/methodology/approach

The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system.

Findings

The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system.

Originality/value

The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

The exploitation of fossil fuels is causing global warming whose negative effects have recently been felt all over the world. Therefore, the search for new sources of energy, renewable and respectful of the environment is crucial for manufacturers. The concept of Best Available Techniques (BAT) presents an adequate solution for manufacturers, for the control, elimination or reduction of the harmful impacts of their activities on the environment. This concept, known as Integrated Pollution Prevention and Control (IPPC), was introduced and imposed from 1996 in Europe. This paper aims to introduce the possibility of transferring the IPPC approach and BAT concepts to Algeria. Therefore, the main objective is to propose some recommendations to optimize the technical and environmental performance of hybrid solar-gas systems, by treating as a case study the first hybrid solar-gas power plant SPPI (Solar Power Plant One) near Hassi R’mel in the south of Algeria. A gap analysis of the Algerian environmental policy compared to the IPPC system, and an assessment of technical and environmental performance of the “SPPI” plant in terms of regulation and BAT are developed in our study.

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.

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.

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.

This paper presents a statistical analysis of reported accidents related to the Skikda's Oil and Gas Refinery that led to a major explosion and loss of life. A historical review of fire, explosion and toxic release incidents that occurred during the period between 2002 and 2013 is presented. A brief description of each accident/incident is provided with the fatalities and personal injuries that resulted being described. These case studies have been analyzed with a view to understand the common causes including errors, which have led to catastrophic events. Accident frequency analysis has been applied to various types of primary accidental events such as fire, explosion and toxic gas releases to assess the potential damage of such events and the likelihood of such damage occurring. The basic principles of such analysis is to utilize the available information and use the most detailed elements to illustrate what the industry should learn from these accidents so that it can learn from them and hopefully prevent future major accidents.