Safety Assessment Curve (SAC) for inherent safety assessment in petrochemical industry
TL;DR: In this article, a new numerical approach for safety assessment called Safety Assessment Curve (SAC) has been proposed, which can offer more useful features because aside from assessing the routes numerically, it could also graphically visualizes the effect of temperature, pressure, heat of reaction, process inventory, flammability, explosiveness, toxicity and reactivity.
Abstract: This paper highlights the development of a new numerical approach for safety assessment called Safety Assessment Curve (SAC). Most of the current methods for assessing inherent safety are index based method. Among the disadvantages of such methods is it employs scaling by dividing physical or chemical properties into subjective ranges and sudden jump in the score value at the sub-range boundary. This new technique can offer more useful features because aside from assessing the routes numerically, it could also graphically visualizes the effect of temperature, pressure, heat of reaction, process inventory, flammability, explosiveness, toxicity and reactivity in designing an inherently safer design for both, grassroot and retrofit cases in petrochemical industry without including subjective scaling and sudden jump in the score value. Due to page limitations, this paper will only discuss the development of SAC for chemical safety parameters. This novel technique can be used as an effective method to find the safer route among several number of alternatives for chemical synthesis or process retrofitting, besides highlighting the potential source of hazards in the process through numerical and graphical approach. The new SAC technique illustrated in this paper has been tested on methyl methacrylate manufacturing confirming its superiority in comparison to index-based method. Tertiery butyl alcohol (TBA) route has the lowest Chemical Safety Total Score suggesting it as the safest routes among the three routes for MMA production compared to acetone cyanohydrin (ACH) and ethylene via methyl propionate (C2/MP) based routes.
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01 Jan 1999
171 citations
TL;DR: In this paper, a graphical methodology for industrial safety risk and environmental management is proposed, which is assumed that a set of risk or pollution reduction measures is available, and that each measure is characterized by its implementation cost and the degree of benefit that it delivers.
Abstract: Pinch Analysis is an established method for enhancing the sustainability of industrial processes via efficient use of various resources. It is based on the principle of target identification followed by subsequent system design aided by a problem decomposition strategy based on the Pinch Point. This approach has recently been extended to apply to a broad range of structurally analogous problems in various domains, such as financial management and carbon-constrained energy planning. In this work, a novel graphical methodology for industrial safety risk and environmental management is proposed. In this method, it is assumed that a set of risk or pollution reduction measures is available, and that each measure is characterized by its implementation cost and the degree of benefit that it delivers. These data are then used to generate a source composite curve. Targeting can then be achieved by shifting this curve relative to a pre-defined sink composite curve, which represents the locus of the plant management’s “willingness to pay,” or budget relative to benefits with respect to risk or pollutant reduction. The methodology is then demonstrated on two case studies. The first case is based on the well-known Bhopal incident, while the second case focuses on the reduction of airborne fluoride emissions from brick firing plant.
38 citations
Cites background from "Safety Assessment Curve (SAC) for i..."
...However, the graphical display is useful for facilitating decision-making with respect to practical applications (Ahmad et al. 2013)....
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TL;DR: In this paper, a new integrated approach of computer-aided product design and inherent safety assessment was discussed, where the formulation of solvent mixtures was optimized to meet the targeted physical properties before being tested using the Soxhlet Extraction method.
Abstract: The concept of inherent safety is important in developing an inherently safer and user-friendly process. This paper discusses a new integrated approach of computer-aided product design and inherent safety assessment. Computer-aided Molecular Design (CAMD) approach was utilized in this work to identify potential alternative to n-hexane, the widely used industrial solvent in extracting residual palm oil from pressed palm fibre. The formulation of solvent mixtures was optimized to meet the targeted physical properties before being tested using the Soxhlet Extraction method. Inherent safety assessment to assess the solvent's flammability, toxicity, reactivity, and explosiveness was conducted on the new solvent mix, Mixture 1 (n-hexane + ethanol), Mixture 2 (n-hexane + acetone) and Mixture 3 (n-hexane + n-butanol). It was found that Mixture 1 and 3 are safer than n-hexane and able to extract more oil than n-hexane and Mixture 2. However, the utilization of the solvent is dependent on the end product from the residual palm oil.
8 citations
TL;DR: In this paper, an inherent system safety index (ISSI) is proposed to evaluate inherently safer design during the concept development stage, which is helpful for the industry in designing a safer plant considering the health, safety, and environmental perspective.
Abstract: Inherently safer design is the most proactive approach to manage risk, as referred by scientists and experts. Researchers have adopted various methods in evaluating inherent safety indices like parameter-based indexing, risk-based indexing, consequence-based indexing, etc. However, the existing approaches have their limitations. The present paper focuses on establishing an inherent system safety index (ISSI) to evaluate inherently safer design during the concept development stage. The analysis starts by identifying a non-harmful system's inherent safety characteristics and related parameters. Four subindexes, determined from the non-harmful system's characteristics, are established using their relevant parameters. The safety of the chemical process system, the health of workers, and the environment's safety can be assured by selecting relevant parameters. Parameters are scored based on their deviation from the non-harmful condition. The sum of the deviations of the parameters gives the value of the inherent safety index. The case study looks at various routes of Methyl Methacrylate (MMA). According to the present case study, MMA production followed by Tertiary butyl alcohol is the safest route given health, safety, and environmental perspective. This approach helps overcome the limitation of parameter-based indexing, which arises from selecting predefined fixed parameters that become invalid in case of system variation or significant modification of the system. Besides, it considers the complexity and vulnerability that arises from the interaction of various factors|, which increase predetermined risk calculated at the design stage when the system is in operation. The subindices can be used individually if a focus is needed in a definite section of a system with a particular application or a smaller portion. This method is helpful for the industry in designing a safer plant considering the health, safety, and environmental perspective at the concept development stage.
8 citations
TL;DR: In this paper, a graphical methodology for risk management in the process industries is proposed, which is assumed that there a set of risk reduction measures is available, and each measure is characterized by its implementation cost and the degree of risk criticality that it addresses.
Abstract: Pinch Analysis is an established method for enhancing the sustainability of industrial processes via efficient use of various resources. It is based on the principle of target identification followed by subsequent system design aided by a problem decomposition strategy using the Pinch Point. This approach has recently been extended to a broad range of structurally analogous problems in various domains, such as financial management, carbon-constrained energy planning, etc. In this work, a novel graphical methodology for risk management in the process industries is proposed. In this approach, it is assumed that there a set of risk reduction measures is available, and that each measure is characterized by its implementation cost and the degree of risk criticality that it addresses. These data are then used to generate a Source Composite Curve. Targeting can then be achieved by shifting this curve relative to a pre-defined Sink Composite Curve, which represents the locus of the plant management’s “willingness to pay”, or budget relative to risk criticality. The methodology is then demonstrated on a case study based on the well-known Bhopal incident.
4 citations
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Book•
26 Oct 2001
TL;DR: In this article, the authors present a review of the safety of industrial hygienic products, including Relief Sizing, and a case history of cases of fire and explosion.
Abstract: 1. Introduction. 2. Toxicology. 3. Industrial Hygiene. 4. Source Models. 5. Toxic Release and Dispersion Models. 6. Fires and Explosions. 7. Designs to Prevent Fires and Explosions. 8. Introduction to Reliefs. 9. Relief Sizing. 10. Hazards Identification. 11. Risk Assessment. 12. Accident Investigations. 13. Case Histories. Appendix I. Normal Safety Review Report. Appendix II. Solution Vapor Pressure Data. Appendix III. Unit Conversion Constants.
930 citations
Book•
01 Jan 1981
TL;DR: In this article, Monte Carlo techniques are used to estimate the probability of a given set of variables for a particular set of classes of data, such as conditional probability and hypergeometric probability.
Abstract: 1. Introduction 1.1 An Overview 1.2 Some Examples 1.3 A Brief History 1.4 A Chapter Summary 2. Probability 2.1 Introduction 2.2 Sample Spaces and the Algebra of Sets 2.3 The Probability Function 2.4 Conditional Probability 2.5 Independence 2.6 Combinatorics 2.7 Combinatorial Probability 2.8 Taking a Second Look at Statistics (Monte Carlo Techniques) 3. Random Variables 3.1 Introduction 3.2 Binomial and Hypergeometric Probabilities 3.3 Discrete Random Variables 3.4 Continuous Random Variables 3.5 Expected Values 3.6 The Variance 3.7 Joint Densities 3.8 Transforming and Combining Random Variables 3.9 Further Properties of the Mean and Variance 3.10 Order Statistics 3.11 Conditional Densities 3.12 Moment-Generating Functions 3.13 Taking a Second Look at Statistics (Interpreting Means) Appendix 3.A.1 MINITAB Applications 4. Special Distributions 4.1 Introduction 4.2 The Poisson Distribution 4.3 The Normal Distribution 4.4 The Geometric Distribution 4.5 The Negative Binomial Distribution 4.6 The Gamma Distribution 4.7 Taking a Second Look at Statistics (Monte Carlo Simulations) Appendix 4.A.1 MINITAB Applications Appendix 4.A.2 A Proof of the Central Limit Theorem 5. Estimation 5.1 Introduction 5.2 Estimating Parameters: The Method of Maximum Likelihood and the Method of Moments 5.3 Interval Estimation 5.4 Properties of Estimators 5.5 Minimum-Variance Estimators: The Crami?½r-Rao Lower Bound 5.6 Sufficient Estimators 5.7 Consistency 5.8 Bayesian Estimation 5.9 Taking A Second Look at Statistics (Beyond Classical Estimation) Appendix 5.A.1 MINITAB Applications 6. Hypothesis Testing 6.1 Introduction 6.2 The Decision Rule 6.3 Testing Binomial Dataâ H0: p = po 6.4 Type I and Type II Errors 6.5 A Notion of Optimality: The Generalized Likelihood Ratio 6.6 Taking a Second Look at Statistics (Statistical Significance versus â Practicalâ Significance) 7. Inferences Based on the Normal Distribution 7.1 Introduction 7.2 Comparing Y-i?½ s/ vn and Y-i?½ S/ vn 7.3 Deriving the Distribution of Y-i?½ S/ vn 7.4 Drawing Inferences About i?½ 7.5 Drawing Inferences About s2 7.6 Taking a Second Look at Statistics (Type II Error) Appendix 7.A.1 MINITAB Applications Appendix 7.A.2 Some Distribution Results for Y and S2 Appendix 7.A.3 A Proof that the One-Sample t Test is a GLRT Appendix 7.A.4 A Proof of Theorem 7.5.2 8. Types of Data: A Brief Overview 8.1 Introduction 8.2 Classifying Data 8.3 Taking a Second Look at Statistics (Samples Are Not â Validâ !) 9. Two-Sample Inferences 9.1 Introduction 9.2 Testing H0: i?½X =i?½Y 9.3 Testing H0: s2X=s2Yâ The F Test 9.4 Binomial Data: Testing H0: pX = pY 9.5 Confidence Intervals for the Two-Sample Problem 9.6 Taking a Second Look at Statistics (Choosing Samples) Appendix 9.A.1 A Derivation of the Two-Sample t Test (A Proof of Theorem 9.2.2) Appendix 9.A.2 MINITAB Applications 10. Goodness-of-Fit Tests 10.1 Introduction 10.2 The Multinomial Distribution 10.3 Goodness-of-Fit Tests: All Parameters Known 10.4 Goodness-of-Fit Tests: Parameters Unknown 10.5 Contingency Tables 10.6 Taking a Second Look at Statistics (Outliers) Appendix 10.A.1 MINITAB Applications 11. Regression 11.1 Introduction 11.2 The Method of Least Squares 11.3 The Linear Model 11.4 Covariance and Correlation 11.5 The Bivariate Normal Distribution 11.6 Taking a Second Look at Statistics (How Not to Interpret the Sample Correlation Coefficient) Appendix 11.A.1 MINITAB Applications Appendix 11.A.2 A Proof of Theorem 11.3.3 12. The Analysis of Variance 12.1 Introduction 12.2 The F Test 12.3 Multiple Comparisons: Tukeyâ s Method 12.4 Testing Subhypotheses with Contrasts 12.5 Data Transformations 12.6 Taking a Second Look at Statistics (Putting the Subject of Statistics togetherâ the Contributions of Ronald A. Fisher) Appendix 12.A.1 MINITAB Applications Appendix 12.A.2 A Proof of Theorem 12.2.2 Appendix 12.A.3 The Distribution of SSTR/(kâ 1) SSE/(nâ k)When H1 is True 13. Randomized Block Designs 13.1 Introduction 13.2 The F Test for a Randomized Block Design 13.3 The Paired t Test 13.4 Taking a Second Look at Statistics (Choosing between a Two-Sample t Test and a Paired t Test) Appendix 13.A.1 MINITAB Applications 14. Nonparametric Statistics 14.1 Introduction 14.2 The Sign Test 14.3 Wilcoxon Tests 14.4 The Kruskal-Wallis Test 14.5 The Friedman Test 14.6 Testing for Randomness 14.7 Taking a Second Look at Statistics (Comparing Parametric and Nonparametric Procedures) Appendix 14.A.1 MINITAB Applications Appendix: Statistical Tables Answers to Selected Odd-Numbered Questions Bibliography Index
869 citations
TL;DR: A new method is presented that closes the gap considerably in early SHE assessment because of the lack of substance data and process information, especially when batch processes are considered.
Abstract: During chemical process development, potential safety, health, and environmental (SHE) hazards must be identified, analyzed, and managed as early as possible to avoid negative consequences (higher risks, higher costs, longer development times). The main problem of early SHE assessment is the lack of substance data and process information, especially when batch processes are considered. A new method is presented that closes this gap considerably. SHE aspects are assessed in 11 effect categories. For each substance of a given chemical process and each effect category, the most reliable data are selected out of a variety of different substance databases or estimation methods. After identifying SHE problems as dangerous properties, their magnitude is analyzed as potential of danger and can be reduced by technological measures. In this paper, first the goal and scope of the method is outlined. Then the detailed definitions of all categories as well as the exact way of combining information is presented and dis...
258 citations
"Safety Assessment Curve (SAC) for i..." refers background or methods in this paper
...Aside from safety aspect, SHE Method (Koller et al., 2000) covers health and environmental parameters....
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...Majority of these methods are index-based method such as the PIIS (Edwards and Lawrence, 1993), ISI (Heikkila, 1999), SHE Method (Koller et al., 2000), i-Safe (Palaniappan et al., 2002a, b) and also Inherent Chemical Process Properties Data (Hassim and Ali, 2009)....
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01 Jan 1999
171 citations