Consequence modelling,vulnerability assessment and fuzzy fault tree analysis of hazardous storages in an industrial area

The hazards associated with major accident hazard (MAH) industries are fire, explosion and toxic gas releases. Of these, toxic gas release is the worst as it has the potential to cause extensive fatalities. Qualitative and quantitative hazard analyses are essential for the identitication and quantification of the hazards associated with chemical industries. This research work presents the results of a consequence analysis carried out to assess the damage potential of the hazardous material storages in an industrial area of central Kerala, India. A survey carried out in the major accident hazard (MAH) units in the industrial belt revealed that the major hazardous chemicals stored by the various industrial units are ammonia, chlorine, benzene, naphtha, cyclohexane, cyclohexanone and LPG. The damage potential of the above chemicals is assessed using consequence modelling. Modelling of pool fires for naphtha, cyclohexane, cyclohexanone, benzene and ammonia are carried out using TNO model. Vapor cloud explosion (VCE) modelling of LPG, cyclohexane and benzene are carried out using TNT equivalent model. Boiling liquid expanding vapor explosion (BLEVE) modelling of LPG is also carried out. Dispersion modelling of toxic chemicals like chlorine, ammonia and benzene is carried out using the ALOHA air quality model. Threat zones for different hazardous storages are estimated based on the consequence modelling. The distance covered by the threat zone was found to be maximum for chlorine release from a chlor-alkali industry located in the area. The results of consequence modelling are useful for the estimation of individual risk and societal risk in the above industrial area.Vulnerability assessment is carried out using probit functions for toxic, thermal and pressure loads. Individual and societal risks are also estimated at different locations. Mapping of threat zones due to different incident outcome cases from different MAH industries is done with the help of Are GIS.Fault Tree Analysis (FTA) is an established technique for hazard evaluation. This technique has the advantage of being both qualitative and quantitative, if the probabilities and frequencies of the basic events are known. However it is often difficult to estimate precisely the failure probability of the components due to insufficient data or vague characteristics of the basic event. It has been reported that availability of the failure probability data pertaining to local conditions is surprisingly limited in India. This thesis outlines the generation of failure probability values of the basic events that lead to the release of chlorine from the storage and filling facility of a major chlor-alkali industry located in the area using expert elicitation and proven fuzzy logic. Sensitivity analysis has been done to evaluate the percentage contribution of each basic event that could lead to chlorine release. Two dimensional fuzzy fault tree analysis (TDFFTA) has been proposed for balancing the hesitation factor invo1ved in expert elicitation .

MQFD: A Model for Synergizing TPM and QFD

This thesis presents the methodology of linking Total Productive Maintenance (TPM) and Quality Function Deployment (QFD). The Synergic power ofTPM and QFD led to the formation of a new maintenance model named Maintenance Quality Function Deployment (MQFD). This model was found so powerful that, it could overcome the drawbacks of TPM, by taking care of customer voices. Those voices of customers are used to develop the house of quality. The outputs of house of quality, which are in the form of technical languages, are submitted to the top management for making strategic decisions. The technical languages, which are concerned with enhancing maintenance quality, are strategically directed by the top management towards their adoption of eight TPM pillars. The TPM characteristics developed through the development of eight pillars are fed into the production system, where their implementation is focused towards increasing the values of the maintenance quality parameters, namely overall equipment efficiency (GEE), mean time between failures (MTBF), mean time to repair (MTIR), performance quality, availability and mean down time (MDT). The outputs from production system are required to be reflected in the form of business values namely improved maintenance quality, increased profit, upgraded core competence, and enhanced goodwill. A unique feature of the MQFD model is that it is not necessary to change or dismantle the existing process ofdeveloping house ofquality and TPM projects, which may already be under practice in the company concerned. Thus, the MQFD model enables the tactical marriage between QFD and TPM.First, the literature was reviewed. The results of this review indicated that no activities had so far been reported on integrating QFD in TPM and vice versa. During the second phase, a survey was conducted in six companies in which TPM had been implemented. The objective of this survey was to locate any traces of QFD implementation in TPM programme being implemented in these companies. This survey results indicated that no effort on integrating QFD in TPM had been made in these companies. After completing these two phases of activities, the MQFD model was designed. The details of this work are presented in this research work. Followed by this, the explorative studies on implementing this MQFD model in real time environments were conducted. In addition to that, an empirical study was carried out to examine the receptivity of MQFD model among the practitioners and multifarious organizational cultures. Finally, a sensitivity analysis was conducted to find the hierarchy of various factors influencing MQFD in a company. Throughout the research work, the theory and practice of MQFD were juxtaposed by presenting and publishing papers among scholarly communities and conducting case studies in real time scenario.

Assessment of Energy Conservation in Indian Cement Industry and Forecasting of Co2 Emissions

Cement industry ranks 2nd in energy consumption among the industries in India. It is one of the major emitter of CO2, due to combustion of fossil fuel and calcination process. As the huge amount of CO2 emissions cause severe environment problems, the efficient and effective utilization of energy is a major concern in Indian cement industry. The main objective of the research work is to assess the energy cosumption and energy conservation of the Indian cement industry and to predict future trends in cement production and reduction of CO2 emissions. In order to achieve this objective, a detailed energy and exergy analysis of a typical cement plant in Kerala was carried out. The data on fuel usage, electricity consumption, amount of clinker and cement production were also collected from a few selected cement industries in India for the period 2001 - 2010 and the CO2 emissions were estimated. A complete decomposition method was used for the analysis of change in CO2 emissions during the period 2001 - 2010 by categorising the cement industries according to the specific thermal energy consumption. A basic forecasting model for the cement production trend was developed by using the system dynamic approach and the model was validated with the data collected from the selected cement industries. The cement production and CO2 emissions from the industries were also predicted with the base year as 2010. The sensitivity analysis of the forecasting model was conducted and found satisfactory. The model was then modified for the total cement production in India to predict the cement production and CO2 emissions for the next 21 years under three different scenarios. The parmeters that influence CO2 emissions like population and GDP growth rate, demand of cement and its production, clinker consumption and energy utilization are incorporated in these scenarios. The existing growth rate of the population and cement production in the year 2010 were used in the baseline scenario. In the scenario-1 (S1) the growth rate of population was assumed to be gradually decreasing and finally reach zero by the year 2030, while in scenario-2 (S2) a faster decline in the growth rate was assumed such that zero growth rate is achieved in the year 2020. The mitigation strategiesfor the reduction of CO2 emissions from the cement production were identified and analyzed in the energy management scenarioThe energy and exergy analysis of the raw mill of the cement plant revealed that the exergy utilization was worse than energy utilization. The energy analysis of the kiln system showed that around 38% of heat energy is wasted through exhaust gases of the preheater and cooler of the kiln sysetm. This could be recovered by the waste heat recovery system. A secondary insulation shell was also recommended for the kiln in the plant in order to prevent heat loss and enhance the efficiency of the plant. The decomposition analysis of the change in CO2 emissions during 2001- 2010 showed that the activity effect was the main factor for CO2 emissions for the cement industries since it is directly dependent on economic growth of the country. The forecasting model showed that 15.22% and 29.44% of CO2 emissions reduction can be achieved by the year 2030 in scenario- (S1) and scenario-2 (S2) respectively. In analysing the energy management scenario, it was assumed that 25% of electrical energy supply to the cement plants is replaced by renewable energy. The analysis revealed that the recovery of waste heat and the use of renewable energy could lead to decline in CO2 emissions 7.1% for baseline scenario, 10.9 % in scenario-1 (S1) and 11.16% in scenario-2 (S2) in 2030. The combined scenario considering population stabilization by the year 2020, 25% of contribution from renewable energy sources of the cement industry and 38% thermal energy from the waste heat streams shows that CO2 emissions from Indian cement industry could be reduced by nearly 37% in the year 2030. This would reduce a substantial level of greenhouse gas load to the environment. The cement industry will remain one of the critical sectors for India to meet its CO2 emissions reduction target. India’s cement production will continue to grow in the near future due to its GDP growth. The control of population, improvement in plant efficiency and use of renewable energy are the important options for the mitigation of CO2 emissions from Indian cement industries

Performance of Supply Chain when using selected Coordination Mechanisms

Coordination among supply chain members is essential for better supply chain performance. An effective method to improve supply chain coordination is to implement proper coordination mechanisms. The primary objective of this research is to study the performance of a multi-level supply chain while using selected coordination mechanisms separately, and in combination, under lost sale and back order cases. The coordination mechanisms used in this study are price discount, delay in payment and different types of information sharing. Mathematical modelling and simulation modelling are used in this study to analyse the performance of the supply chain using these mechanisms. Initially, a three level supply chain consisting of a supplier, a manufacturer and a retailer has been used to study the combined effect of price discount and delay in payment on the performance (profit) of supply chain using mathematical modelling. This study showed that implementation of individual mechanisms improves the performance of the supply chain compared to ‘no coordination’. When more than one mechanism is used in combination, performance in most cases further improved. The three level supply chain considered in mathematical modelling was then extended to a three level network supply chain consisting of a four retailers, two wholesalers, and a manufacturer with an infinite part supplier. The performance of this network supply chain was analysed under both lost sale and backorder cases using simulation modelling with the same mechanisms: ‘price discount and delay in payment’ used in mathematical modelling. This study also showed that the performance of the supply chain is significantly improved while using combination of mechanisms as obtained earlier. In this study, it is found that the effect (increase in profit) of ‘delay in payment’ and combination of ‘price discount’ & ‘delay in payment’ on SC profit is relatively high in the case of lost sale. Sensitivity analysis showed that order cost of the retailer plays a major role in the performance of the supply chain as it decides the order quantity of the other players in the supply chain in this study. Sensitivity analysis also showed that there is a proportional change in supply chain profit with change in rate of return of any player. In the case of price discount, elasticity of demand is an important factor to improve the performance of the supply chain. It is also found that the change in permissible delay in payment given by the seller to the buyer affects the SC profit more than the delay in payment availed by the buyer from the seller. In continuation of the above, a study on the performance of a four level supply chain consisting of a manufacturer, a wholesaler, a distributor and a retailer with ‘information sharing’ as coordination mechanism, under lost sale and backorder cases, using a simulation game with live players has been conducted. In this study, best performance is obtained in the case of sharing ‘demand and supply chain performance’ compared to other seven types of information sharing including traditional method. This study also revealed that effect of information sharing on supply chain performance is relatively high in the case of lost sale than backorder. The in depth analysis in this part of the study showed that lack of information sharing need not always be resulting in bullwhip effect. Instead of bullwhip effect, lack of information sharing produced a huge hike in lost sales cost or backorder cost in this study which is also not favorable for the supply chain. Overall analysis provided the extent of improvement in supply chain performance under different cases. Sensitivity analysis revealed useful insights about the decision variables of supply chain and it will be useful for the supply chain management practitioners to take appropriate decisions.

Evanescent Wave Fibre Optic Sensors for Trace Analysis of Fe3+ in Water

In this communication, we discuss the details of fabricating an off-line fibre optic sensor (FOS) based on evanescent wave absorption for detecting trace amounts of Fe3+ in water. Two types of FOS are developed; one type uses the unclad portion of a multimode silica fibre as the sensing region whereas the other employs the microbent portion of a multimode plastic fibre as the sensing region. Sensing is performed by measuring the absorption of the evanescent wave in a reagent medium surrounding the sensing region. To evaluate the relative merits of the two types of FOS in Fe3+ sensing, a comparative study of the sensors is made, which reveals the superiority of the latter in many respects, such as smaller sensing length, use of a double detection scheme (for detecting both core and cladding modes) and higher sensitivity of cladding mode detection at an intermediate range of concentration along with the added advantage that plastic fibres are inexpensive. A detection limit of 1 ppb is observed in both types of fibre and the range of detection can be as large as 1 ppb–50 ppm. All the measurements are carried out using a LabVIEW set-up.

Evanescent wave fibre optic sensors for trace analysis of Fe3+ in water

In this communication, we discuss the details of fabricating an off-line fibre optic sensor (FOS) based on evanescent wave absorption for detecting trace amounts of Fe3+ in water. Two types of FOS are developed; one type uses the unclad portion of a multimode silica fibre as the sensing region whereas the other employs the microbent portion of a multimode plastic fibre as the sensing region. Sensing is performed by measuring the absorption of the evanescent wave in a reagent medium surrounding the sensing region. To evaluate the relative merits of the two types of FOS in Fe3+ sensing, a comparative study of the sensors is made, which reveals the superiority of the latter in many respects, such as smaller sensing length, use of a double detection scheme (for detecting both core and cladding modes) and higher sensitivity of cladding mode detection at an intermediate range of concentration along with the added advantage that plastic fibres are inexpensive. A detection limit of 1 ppb is observed in both types of fibre and the range of detection can be as large as 1 ppb–50 ppm. All the measurements are carried out using a LabVIEW set-up.

Evanescent wave fibre optic sensors for trace analysis of Fe3+ in water

In this communication, we discuss the details of fabricating an off-line fibre optic sensor (FOS) based on evanescent wave absorption for detecting trace amounts of Fe3+ in water. Two types of FOS are developed; one type uses the unclad portion of a multimode silica fibre as the sensing region whereas the other employs the microbent portion of a multimode plastic fibre as the sensing region. Sensing is performed by measuring the absorption of the evanescent wave in a reagent medium surrounding the sensing region. To evaluate the relative merits of the two types of FOS in Fe3+ sensing, a comparative study of the sensors is made, which reveals the superiority of the latter in many respects, such as smaller sensing length, use of a double detection scheme (for detecting both core and cladding modes) and higher sensitivity of cladding mode detection at an intermediate range of concentration along with the added advantage that plastic fibres are inexpensive. A detection limit of 1 ppb is observed in both types of fibre and the range of detection can be as large as 1 ppb–50 ppm. All the measurements are carried out using a LabVIEW set-up.

Evanescent wave fibre optic sensors for trace analysis of Fe3+ in water

In this communication, we discuss the details of fabricating an off-line fibre optic sensor (FOS) based on evanescent wave absorption for detecting trace amounts of Fe3+ in water. Two types of FOS are developed; one type uses the unclad portion of a multimode silica fibre as the sensing region whereas the other employs the microbent portion of a multimode plastic fibre as the sensing region. Sensing is performed by measuring the absorption of the evanescent wave in a reagent medium surrounding the sensing region. To evaluate the relative merits of the two types of FOS in Fe3+ sensing, a comparative study of the sensors is made, which reveals the superiority of the latter in many respects, such as smaller sensing length, use of a double detection scheme (for detecting both core and cladding modes) and higher sensitivity of cladding mode detection at an intermediate range of concentration along with the added advantage that plastic fibres are inexpensive. A detection limit of 1 ppb is observed in both types of fibre and the range of detection can be as large as 1 ppb–50 ppm. All the measurements are carried out using a LabVIEW set-up.

Speech Analysis using Modern Techniques of Nonlinear Dynamics

Medical fields requires fast, simple and noninvasive methods of diagnostic techniques. Several methods are available and possible because of the growth of technology that provides the necessary means of collecting and processing signals. The present thesis details the work done in the field of voice signals. New methods of analysis have been developed to understand the complexity of voice signals, such as nonlinear dynamics aiming at the exploration of voice signals dynamic nature. The purpose of this thesis is to characterize complexities of pathological voice from healthy signals and to differentiate stuttering signals from healthy signals. Efficiency of various acoustic as well as non linear time series methods are analysed. Three groups of samples are used, one from healthy individuals, subjects with vocal pathologies and stuttering subjects. Individual vowels/ and a continuous speech data for the utterance of the sentence "iruvarum changatimaranu" the meaning in English is "Both are good friends" from Malayalam language are recorded using a microphone . The recorded audio are converted to digital signals and are subjected to analysis.Acoustic perturbation methods like fundamental frequency (FO), jitter, shimmer, Zero Crossing Rate(ZCR) were carried out and non linear measures like maximum lyapunov exponent(Lamda max), correlation dimension (D2), Kolmogorov exponent(K2), and a new measure of entropy viz., Permutation entropy (PE) are evaluated for all three groups of the subjects. Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. The results shows that nonlinear dynamical methods seem to be a suitable technique for voice signal analysis, due to the chaotic component of the human voice. Permutation entropy is well suited due to its sensitivity to uncertainties, since the pathologies are characterized by an increase in the signal complexity and unpredictability. Pathological groups have higher entropy values compared to the normal group. The stuttering signals have lower entropy values compared to the normal signals.PE is effective in charaterising the level of improvement after two weeks of speech therapy in the case of stuttering subjects. PE is also effective in characterizing the dynamical difference between healthy and pathological subjects. This suggests that PE can improve and complement the recent voice analysis methods available for clinicians. The work establishes the application of the simple, inexpensive and fast algorithm of PE for diagnosis in vocal disorders and stuttering subjects.

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Fishery Commodity Chain Trap vis-à-vis Global Quality Standards: An Analysis of the Kerala Marine Fishery

The article examines the commodity chain trap of marine fishery in Kerala, at both material and value terms, and its ramifications in the globalised fishery chains. The marketing chains both material and value, are very complex in nature since they involve many types of markets and large number of intermediaries and participants. The article also scrutinizes the sensitivity of consumers’ and country’s responses in terms of dietary and hygienic standards relating to seafood trade. In addition, it discusses the devastating effect about the recent stipulations like the US Bio- Terrorism Act and Shrimp anti-dumping duty on the Kerala fishery products