J. Maiti

Modeling uncertainty in risk assessment: An integrated approach with fuzzy set theory and Monte Carlo simulation

Modeling uncertainty during risk assessment is a vital component for effective decision making. Unfortunately, most of the risk assessment studies suffer from uncertainty analysis. The development of tools and techniques for capturing uncertainty in risk assessment is ongoing and there has been a substantial growth in this respect in health risk assessment. In this study, the cross-disciplinary approaches for uncertainty analyses are identified and a modified approach suitable for industrial safety risk assessment is proposed using fuzzy set theory and Monte Carlo simulation. The proposed method is applied to a benzene extraction unit (BEU) of a chemical plant. The case study results show that the proposed method provides better measure of uncertainty than the existing methods as unlike traditional risk analysis method this approach takes into account both variability and uncertainty of information into risk calculation, and instead of a single risk value this approach provides interval value of risk values for a given percentile of risk. The implications of these results in terms of risk control and regulatory compliances are also discussed.

Risk analysis using FMEA

The study enhances the capability of FMEA as a risk assessment tool.It uses fuzzy similarity value based measurement.It demonstrates the applicability of possibility theory in decision making.It considers two case studies.The results are compared with traditional methods. Fuzzy numerical technique for FMEA has been proposed to deal with the drawbacks of crisp FMEA and fuzzy rule based FMEA approaches. Fuzzy numerical approaches based on de-fuzzification also suffer from the drawback of providing arbitrary priority ranks of failure modes even when their membership functions overlap. To overcome this drawback we developed a new methodology integrating the concepts of similarity value measure of fuzzy numbers and possibility theory. Similarity value measure has been applied to group together failure modes having similar amount of risk value. The possibility theory has been used for checking for conformance guidelines. Two case studies have been shown to demonstrate the methodology thus developed. The proposed methodology is more robust in nature as it does not require arbitrary precise operations like de-fuzzification to prioritise the failure modes. Application of possibility theory is new to the domain of risk analysing using FMEA.

A methodology for overall consequence modeling in chemical industry

Risk assessment in chemical process industry is a very important issue for safeguarding human and the ecosystem from damages caused to them. Consequence assessment is an integral part of risk assessment. However, the commonly used consequence estimation methods involve time-consuming complex mathematical models and simple assimilation of losses without considering all the consequence factors. This lead to the deterioration of quality of estimated risk value. So, the consequence modeling has to be performed in detail considering all major losses with optimal time to improve the decisive value of risk. The losses can be broadly categorized into production loss, assets loss, human health and safety loss, and environment loss. In this paper, a conceptual framework is developed to assess the overall consequence considering all the important components of major losses. Secondly, a methodology is developed for the calculation of all the major losses, which are normalized to yield the overall consequence. Finally, as an illustration, the proposed methodology is applied to a case study plant involving benzene extraction. The case study result using the proposed consequence assessment scheme is compared with that from the existing methodologies.

The synergic role of sociotechnical and personal characteristics on work injuries in mines.

Occupational injuries in mines are attributed to many factors. In this study, an attempt was made to identify the various factors related to work injuries in mines and to estimate their effects on work injuries to mine workers. An accident path model was developed to estimate the pattern and strength of relationships amongst the personal and sociotechnical variables in accident/injury occurrences. The input data for the model were the correlation matrix of 18 variables, which were collected from the case study mines. The case study results showed that there are sequential interactions amongst the sociotechnical and personal factors leading to accidents/injuries in mines. Amongst the latent endogenous constructs, job dissatisfaction and safe work behaviour show a significant positive and negative direct relationship with work injury, respectively. However, the construct safety environment has a significant negative indirect relationship with work injury. The safety environment is negatively affected by work hazards and positively affected by social support. The safety environment also shows a significant negative relationship with job stress and job dissatisfaction. However, negative personality has no significant direct or indirect effect on work injury, but it has a significant negative relationship with safe work behaviour. The endogenous construct negative personality is positively influenced by job stress and negatively influenced by social support.

Process control strategies for a steel making furnace using ANN with bayesian regularization and ANFIS

This paper illustrates the control strategies of an Electric Arc Furnace. It involves the prediction of the control action which aids in reduction of carbon, manganese and other impurities from the in-process molten steel. Predictive models using Artificial Neural Networks (ANN) with Bayesian Regularization and Adaptive Neuro Fuzzy Inference System (ANFIS) were developed. The control action is the amount of oxygen to be lanced at different sampling instants. The predictive models were constructed based on the values of the individual chemical constituents of the collected molten samples. Two control strategies were devised: one with full sampling and the other with limited or reduced sampling. For the full sampling case two predictive models were devised separately with ANN with Bayesian Regularization and ANFIS. For the limited sampling strategy a combination of ANN with Bayesian Regularization and ANFIS were employed. For full sampling strategy, ANFIS model performs better than ANN. The application of the limited sampling strategy gave satisfactory Mean Percentage Error (MPE) thereby justifying its practical implementation. The main advantage of reduced or limited sampling is that it helps in the reduction of cost, time and manpower associated the sample collection and its subsequent analysis.

Modelling robustness for manufacturing processes: a critical review

‘Robustness’ is an important concept used in quality engineering for the improvement of quality in a manufacturing process. A process which is insensitive to noise variation is called a robust process. The robustness is modelled by several researchers and practioners for its design and implementation in a manufacturing process. A review of all these approaches is essential in order to assess their strengths, limitations and applicability under different process conditions and constraints. Over the years, many of these approaches have found widespread application in measuring, assessing and modelling of process robustness in manufacturing and other industries. In this paper, an attempt has been made to review critically the existing approaches as proposed and applied for measuring and evaluating robustness of manufacturing processes. Based on the critical appraisal, the key issues are identified and a generic framework for modelling and measuring of process robustness in single- and multi-stage manufacturing processes is presented.

Development of environmental consequence index (ECI) using fuzzy composite programming

Estimation of environmental consequences of hazardous substances in chemical industries is a very difficult task owing to (i) diversity in the types of hazards and their effects, (ii) location, and (ii) uncertainty in input information. Several indices have been developed over the years to estimate the environmental consequences. In this paper, a critical literature review was done on the existing environmental indices to identify their applications and limitations. The existing indices lack in consideration of all environmental consequence factors such as material hazard factors, dispersion factors, environmental effects, and their uncertainty. A new methodology is proposed for the development of environmental consequence index (ECI), which can overcome the stated limitations. Moreover, the recently developed fuzzy composite programming (FCP) is used to take care of the uncertainty in estimation. ECI is applied to benzene extraction unit (BEU) of a petrochemical industry situated in eastern part of India. The ECI for all the eight sections of BEU are estimated and ranked. The results are compared with well-established indices such as Dow fire and explosion index, safety weight hazard index (SWeHI), and environmental accident index (EAI). The proposed ECI may outperform other indices based on its detailed consideration of the factors and performed equally to Dow F&E index, and EAI in most of the cases for the present application.

Data mining driven DMAIC framework for improving foundry quality – a case study

Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) methodology has been widely used across industries as the best systematic and data driven problem solving approach for quality improvement. Statistical Design of Experiment (DOE) is used in the ‘Improve’ stage for obtaining optimal process settings for significant variables contributing towards quality improvement. But, DOE is an offline activity requiring time and other resources for conducting experiments and analyses. Further, there are many small and medium scale enterprises that cannot afford to conduct DOE. Under such practical constraints, it is desirable to apply DMAIC using online process data under day-to-day production situations or with little changes in process settings without compromising production. In this article, we propose a DMAIC framework, driven by data mining techniques for defect diagnosis and quality improvement where historical and online process data can be effectively utilised. We have used two decision tree algorithms namely, Classification and Regression Tree and Chi-squared Automatic Interaction Detection in developing the proposed framework. The proposed approach is applied in an Indian grey iron foundry where conducting DOE is not a feasible option for the management. The result demonstrates a significant reduction in casting defect and validates the practical viability of this approach.

An epidemiological study of injury in mines: implications for safety promotion

The role of various factors in coal mine-related injuries was investigated using a case – control design. The study setting was two neighbouring underground coal mines in India. Cases comprised mine workers (n = 150) who had sustained a prior mine-related injury from a population of 1000 underground workers. Controls were selected from those mineworkers with no history of a prior mine-related injury using frequency matching (n = 150) from the same source population. Data were collected from the cases and controls using a structured survey questionnaire. Based on the responses of the participants, each factor was grouped into three categories. High – low plots and Chi-square tests were conducted to explore the differences between the cases and controls. Bivariate logistic regression was run to estimate the crude odds of injuries, while multivariate logistic regression estimated the adjusted odds of injuries to the workers for the various variable categories. High – low plots and the Chi-square test clearly revealed that the cases and controls significantly differed in their responses for the variables studied. Accident-involved workers take more risks, are negatively affected, job dissatisfied, feel more production pressure, job stress, work hazards and are less job involved and are more dissatisfied with safety environment and social climate of the mines compared to the controls. The multivariate odds of injuries to high risk taking, negatively affected and job dissatisfied workers are 1.21, 9.34 and 2.00 times more compared to their lowest counterparts. Similarly, workers satisfied with the overall safety practice and safety equipment availability and maintenance are 1.5 and 3.12 times less likely to be injured than the workers with little or no satisfaction with the above factors. It is therefore concluded that negative affectivity and job dissatisfaction are the two major personal level factors that contribute more towards accident/injury in the mines studied. Identification and elimination/reduction of negative attitudes are of utmost importance.

Process monitoring and fault detection strategies: a review

Purpose – Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.Design/methodology/approach – Taxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion int...