Dynamic risk analysis using alarm databases to improve process safety and product quality: Part II—Bayesian analysis

Part II presents step (iii) of the dynamic risk analysis methodology; that is, a novel Bayesian analysis method that utilizes near‐misses from distributed control system (DCS) and emergency shutdown (ESD) system databases—to calculate the failure probabilities of safety, quality, and operability systems (SQOSs) and probabilities of occurrence of incidents. It accounts for the interdependences among the SQOSs using copulas, which occur because of the nonlinear relationships between the variables and behavior‐based factors involving human operators. Two types of copula functions, multivariate normal and Cuadras–Augé copula, are used. To perform Bayesian simulation, the random‐walk, multiple‐block, Metropolis–Hastings algorithm is used. The benefits of copulas in sharing information when data are limited, especially in the cases of rare events such as failures of override controllers, and automatic and manual ESD systems, are presented. In addition, product‐quality data complement safety data to enrich near‐miss information and to yield more reliable results. Step (iii) is applied to a fluidized‐catalytic‐cracking unit (FCCU) to show its performance. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Improved predictions of alarm and safety system performance through process and operator response‐time modeling

Dynamic risk analysis (DRA) has been used widely to analyze the performance of alarm and safety interlock systems of manufacturing processes. Because the most critical alarm and safety interlock systems are rarely activated, little or no data from these systems are often available to apply purely‐statistical DRA methods. Moskowitz et al. (2015)¹ introduced a repeated‐simulation, process‐model‐based technique for constructing informed prior distributions, generating low‐variance posterior distributions for Bayesian analysis,¹ and making alarm‐performance predictions. This article presents a method of quantifying process model quality, which impacts prior and posterior distributions used in Bayesian Analysis. The method uses higher‐frequency alarm and process data to select the most relevant constitutive equations and assumptions. New data‐based probabilistic models that describe important special‐cause event occurrences and operators’ response‐times are proposed and validated with industrial plant data. These models can be used to improve estimates of failure probabilities for alarm and safety interlock systems. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3461–3472, 2016     

Scalable inference for space‐time Gaussian Cox processes

The log‐Gaussian Cox process is a flexible and popular stochastic process for modeling point patterns exhibiting spatial and space‐time dependence. Model fitting requires approximation of stochastic integrals which is implemented through discretization over the domain of interest. With fine scale discretization, inference based on Markov chain Monte Carlo is computationally burdensome because of the cost of matrix decompositions and storage, such as the Cholesky, for high dimensional covariance matrices associated with latent Gaussian variables. This article addresses these computational bottlenecks by combining two recent developments: (i) a data augmentation strategy that has been proposed for space‐time Gaussian Cox processes that is based on exact Bayesian inference and does not require fine grid approximations for infinite dimensional integrals, and (ii) a recently developed family of sparsity‐inducing Gaussian processes, called nearest‐neighbor Gaussian processes, to avoid expensive matrix computations. Our inference is delivered within the fully model‐based Bayesian paradigm and does not sacrifice the richness of traditional log‐Gaussian Cox processes. We apply our method to crime event data in San Francisco and investigate the recovery of the intensity surface.     

A Bayesian Approach to Event Prediction

Abstract.  In a series of papers, Lindgren (1975a, 1985) and de Maré (1980) set the principles of optimal alarm systems and obtained the basic results. Application of these ideas to linear discrete time-series models was carried out by Svensson et al. (1996) . In this paper, we suggest a Bayesian predictive approach to event prediction and optimal alarm systems for discrete time series. There are two novelties in this approach: first, the variation in the model parameters is incorporated in the analysis; second, this method allows 'on-line prediction' in the sense that, as we observe the process, our posterior probabilities and predictions are updated at each time point.     

Model‐predictive safety system for proactive detection of operation hazards

A method of designing model‐predictive safety systems that can detect operation hazards proactively is presented. Such a proactive safety system has two major components: a set of operability constraints and a robust state estimator. The safety system triggers alarm(s) in real time when the process is unable to satisfy an operability constraint over a receding time‐horizon into the future. In other words, the system uses a process model to project the process operability status and to generate alarm signals indicating the presence of a present or future operation hazard. Unlike typical existing safety systems, it systematically accounts for nonlinearities and interactions among process variables to generate alarm signals; it provides alarm signals tied to unmeasurable, but detectable, state variables; and it generates alarm signals before an actual operation hazard occurs. The application and performance of the method are shown using a polymerization reactor example. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2024–2042, 2016     

Preparation of 1,3‐Diazido‐2‐Nitro‐2‐Azapropane from Urea

An alternative method to prepare 1,3‐diazido‐2‐nitro‐2‐azapropane (DANP), a promising liquid component for high‐energy condensed systems, is suggested and involves the following stages: (i) nitration of urea, (ii) condensation of the nitration product with formaldehyde, (iii) acylation (chlorination) of 1,3‐dihydroxy‐2‐nitro‐2‐azapropane, (iv) chlorination of 1,3‐diacetoxy‐2‐nitro‐2‐azapropane, and (v) azidation of the dichloro derivative to DANP. This synthesis method is selective and enables isolation of 1,3‐diazido‐2‐nitrazapropane devoid of impurities.     

Composite planning and scheduling algorithm addressing intra‐period infeasibilities of gasoline blend planning models

Multi‐period planning models result in solutions which are feasible at the boundaries of the periods but may be infeasible within the periods. The composite algorithm presented here (i) solves coarse multi‐period MILP model structure for production planning; (ii) sequences operations via a genetic algorithm to minimise switching; (iii) verifies schedule feasibility via agent‐based simulation and local logical decision making; and (iv) if infeasible, re‐partitions the time horizon into multi‐periods and resolves from (i) until feasible. Application of the algorithm to gasoline blending illustrates its effectiveness in computing feasible plans and schedules for such systems. © 2012 Canadian Society for Chemical Engineering     

A practical approach to improve alarm system performance: Application to power plant

Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal condition in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. However, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers(i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.     

N‐Heterocyclic Carbene‐Mediated Organocatalytic Transfer of Tin onto Aldehydes: An Easy Access to syn‐Diols and Mechanistic Studies

An update of our preliminary communication concerning an efficient organocatalytic procedure for the transfer of tin onto aldehydes is presented. This update combines (i) a full study of the preparation of γ‐silyloxyallylstannanes from β‐substituted enals, (ii) a “one‐pot” sequence (in inter‐ and intramolecular version) including N‐heterocyclic carbene (NHC)‐catalyzed silylstannation reaction/Lewis acid‐promoted allylstannation reaction to furnish the corresponding syn‐diols, and (iii) mechanistic studies on the organocatalytic 1,2‐addition.     

Development of polymer blend morphology during compounding in a twin-screw extruder. Part IV: A new computational model with coalescence

In Part II of this series of publications, the first generation model of morphology evolution during polymer blending in a twin-screw extruder was presented. The model was based on a simplified flow analysis, and an assumption that dispersion occurs via drop fibrillation followed by disintegration. In the present Part IV, several modifications of the model are discussed. (i) The flow analysis was refined by computing the pressure profiles. (ii) The flow paths and strain history of the dispersed droplets within the screw elements are computed directly, which makes it possible to determine the drop susceptibility to deformation and break. (iii) Besides the fibrillation mechanism, a drop-splitting mechanism for low supercritical capillary numbers is incorporated. (iv) The choice of breakup mechanism is based on micro-rheological criteria. (v) The coalescence effects are taken into account. (vi) The theoretical model is self-consistent, without adjustable parameters. The validity of theoretical assumptions was evaluated by comparing the model predictions with the experimental droplet diameters at different positions in the twin-screw extruder.     

Structure–property relation in varieties of silk fibers

Crystallite shape ellipsoid in different varieties of silk fibers namely (i) Chinese (ii) Indian, and (iii) Japanese, has been computed using wide‐angle X‐ray data and Hosemann's one‐dimensional paracrystalline model. The estimated microcrystalline parameters are correlated with the observed physical property of the silk fibers. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 79: 1979–1985, 2001     

Screening and identification of Pseudomonas aeruginosa AB4 for improved production,characterization and application of a glycolipid biosurfactant using low‐cost agro‐based raw materials

BACKGROUND: The study is focused on (i) screening and taxonomic identity of a bacterial strain for biosurfactant production, and (ii) evaluation of its potential for production of a biosurfactant using agro‐based feedstock(s) and characterization of it for application in the removal of heavy metals. RESULTS: The production of biosurfactant by an isolate Pseudomonas aeruginosa AB4 (identified on the basis of 16S rRNA analysis) using various cost‐effective substrates were examined at conditions 40 °C, 120 rpm for 7 days. It revealed maximum (40 gL^?1) rhamnolipids production and 46% reduction of initial surface tension. Its optimum production was achieved at (i) C:N ratio 10:0.6, (ii) pH 8.5 and (iii) 40 °C. The cell–free supernatant examined for biosurfactant activity by (i) haemolytic assay, (ii) CTAB‐ methylene blue assay, (iii) drop collapse test, (iv) oil spreading technique and (v) EI 24 assay showed its glycolipid nature and stable emulsification. Analysis of partially purified rhamnolipids by (i) thin layer chromatography (TLC), (ii) high performance thin layer chromatography (HPTLC), (iii) high performance liquid chromatography (HPLC), (iv) Fourier transform infrared (FT‐IR) and (v) gas chromatography–mass spectrometry (GC‐MS) confirmed its structure as methyl ester of 3‐hydroxy decanoic acid (a glycolipid) with two major structural congeners (Rha‐C₁₀‐C₁₀ and Rha‐C₁₀‐C₈) of mono‐rhamnolipids. Finally, it showed sequestration of Cd and Pb, suggesting its application in biosurfactant‐assisted heavy metal bioremediation. CONCLUSION: This work has screened and identified a bacterium with superior biosurfactant production capabilities, characterized the glycolipidic biosurfactants as rhamnolipid and indicated the feasibility of biosurfactant production using novel renewable, relatively inexpensive and easily available resources such as non‐edible vegetable de‐oiled seed cakes and showed its utility in remediation of heavy metals. Copyright © 2010 Society of Chemical Industry     

Recent developments of metal oxide semiconductors as photocatalysts in advanced oxidation processes (AOPs) for treatment of dye waste‐water

The textile industry presents a global pollution problem owing to the dumping or accidental discharge of dye waste‐water into waterways, which is having a major impact on the quality and aesthetics of water resources. The World Bank estimates that 17 to 20% of industrial water pollution comes from textile dyeing and treatment. This percentage represents an appalling environmental challenge for clothing designers and other textile manufacturers. Recently, the application of metal oxide semiconductors in the advanced oxidation process (AOP) has gained wide interest for the treatment of dye waste‐water owing to its good degradation efficiency, low toxicity and physical and chemical properties. AOP refers to a set of chemical treatment procedures designed to remove organic and inorganic materials from waste‐water by oxidation. In this paper, recently developed metal oxide semiconductors are discussed, in which the semiconductors are generally divided into three categories: (i) titanium dioxide; (ii) zinc oxide; and (iii) other metal oxides (such as vanadium oxide, tungsten oxide, molybdenum oxide, indium oxide and cerium oxide). The syntheses and modification methods as well as the efficiency of each category are discussed and analyzed. Copyright © 2011 Society of Chemical Industry     

Interactions between Clay Bodies and Lead Glazes

A set of laboratory experiments has been developed to determine the nature and importance of the interaction between clay bodies and lead glazes during firing following different thermal paths (firing time and temperature, cooling rates) and using different glaze compositions and different bodies (illitic, kaolinitic, and calcareous clays). It is shown that the interaction consists of a digestion/diffusion process. This process involves (i) decomposition of the phases forming the clay body (digestion), (ii) chemical diffusion of elements between clay body and glaze, and (iii) formation of a layer (interface between clay and glaze) of small K-Pb feldspar crystallites.     

Comparison of disodium octaborate tetrahydrate‐based and tannin‐boron‐based formulations as fire retardant for wood structures

Boron‐based formulations are used to improve the service life of timber. On the one hand, boron‐treated wood increases resistance to biological attacks (e.g. by fungi and insects), and on the other hand, it renders wood more resistant to burning. In the present study, we analyse the fire behaviour of some water‐borne formulations containing boron. A completely inorganic formulation (disodium octaborate tetrahydrate (DOT)) is compared with new‐generation tannin‐based wood preservatives in which the flavonoid network is used to fix the boron to wood. The study of the fire behaviour was carried out according to four specific fire tests: (i) dripping; (ii) short‐term exposure; (iii) long‐term exposure and (iv) the limiting oxygen index. The Scots pine (Pinus sylvestris L.) specimens treated with DOT have shown a complete efficacy against fire after all tests were completed. It should also be noted that very positive results have also been achieved by the tannin‐based solutions. DOT has to be preferred when high performance is required, but exclusively for interior applications. The use of tannin‐based formulations can be suitable for outdoor fire protection and also for indoor applications when specific aesthetic requirements should be fulfilled. Copyright © 2013 John Wiley & Sons, Ltd.     

Plant-specific dynamic failure assessment using Bayesian theory

Abnormal events of varying magnitudes result in incipient faults, near-misses, incidents, and accidents in chemical plants. Their detection and diagnosis has been an active area of research [Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., 2003a. A review of process fault detection and diagnosis, Part II: Quantitative model and search strategies. Computers and Chemical Engineering 27(3), 313-326; Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K., 2003b. A review of process fault detection and diagnosis, Part III: Process history based methods. Computers and Chemical Engineering 27(3), 327-346]. However, estimation of the failure probabilities of safety systems to predict these consequences (end-states), has received little attention in the CPI. In this work, methods for plant-specific, dynamic risk assessment are developed to predict the frequencies of abnormal events utilizing accident precursor data, helping to achieve inherently safer operations. These methods, which involve repetitive risk analysis after abnormal events occur, are especially beneficial for operations involving complex nonlinearities and multi-component interactions. Herein, the failure probabilities of safety systems and end-states are estimated using copulas and Bayesian analysis to ensure better predictions. The joint probability distribution for the failure probability of a safety system(s) having different consequences is modeled using the Cuadras and Auges copula [Nelsen, R.B., 1999. An Introduction to Copulas. Lecture Notes in Statistics, Springer, New York]. Accident precursor data are used to modify dynamically the initial estimates of failure probabilities to obtain posterior failure probabilities of the safety systems of an exothermic reactor. Finally, fuzzy memberships to various critical zones are formulated as a function of end-state probabilities to judge the safety status of a chemical plant.     

Insensitive High Explosives II: 3,3′‐Diamino‐4,4′‐azoxyfurazan (DAAF)

This paper reviews the synthesis, properties, performance, and safety of the insensitive explosive 3,3′‐diamino‐4,4′‐azoxyfurazan (DAAF, C₄H₄N₈O₃), CAS‐No. [78644‐89‐0], and 18 formulations based on it. Though having a moderate crystal density only, DAAF offers high positive heat of formation and hence superior performance when compared with TATB. It is friction and impact insensitive but is more sensitive to shock than TATB and has an exceptionally small critical diameter and performs very well at low temperatures unlike other insensitive explosives. 39 references to the public domain are given. For Part I see Ref. [1].     

An Adaptive Recipe Implementation in Case‐Based Formalism for Abnormal Condition Management

This paper deals with accurate recipe implementation for abnormal condition management in a batch process using a case‐based reasoning (CBR) approach. A set of new problems can be solved by reusing proven process solutions. The proposed system integrates quantitative and qualitative parameters for adaptation of cases. A novel methodology to generate accurate recipes and to adapt to the processes is introduced during normal and abnormal conditions. In particular, the differences between current conditions and the references (recipes) should be managed to prevent any hazardous conditions arising. The processes are evaluated using their similarity to the past cases. This intelligent approach distinguishes plausible cases, generates accurate recipes, and adapts to new situations. The aim is to use the offline historical process data and safety related information in order to propose changes and adjustments in the processes.     

A Time‐Symmetric Self‐Normalization Approach for Inference of Time Series

Self‐normalization has been celebrated as an alternative approach for inference of time series because of its ability to avoid direct estimation of the nuisance asymptotic variance. However, when being applied to quantities other than the mean, the conventional self‐normalizer typically exhibits certain degrees of asymmetry, an undesirable feature especially for time‐reversible processes. This paper considers a new self‐normalizer for time series, which (i) provides a time‐symmetric generalization to the conventional self‐normalizer, (ii) is able to automatically reduce to the conventional self‐normalizer in the mean case where the latter is already time‐symmetric to yield a unified inference procedure, and (iii) possibly leads to narrower confidence intervals when compared with the conventional self‐normalizer. For the proposed time‐symmetric self‐normalizer, we establish the asymptotic theory for its induced inference procedure and examine its finite sample performance through numerical experiments.     

Variation of MUF and PMUF resins mass fractions during preparation

The variation of molecular mass distribution with the progress of the reaction was studied for the following: (i) sequential‐type melamine–urea–formaldehyde (MUF) resin formulations in which the sequence of addition of chemicals follows well‐defined species reactivity principles; (ii) a nonsequential MUF formulation in which simultaneous melamine and urea competition for formaldehyde yields a MF resin cocondensed with small amounts of urea. This resin became soaked with reacted and unreacted monomeric urea species. (iii) A PMUF resin, namely a MUF resin with a small proportion of phenol (7.8% by weight on melamine and urea) cocondensed with the main MUF fraction. All the formulations used were industrial resins formulations in current use. Development and variation of molecular mass fractions, from which performance and other useful resin parameters depend, have been found to depend on the type of resin formulation used for these type of aminoplastic resins. The two very different MUF resin formulations yielded different variations in molecular mass fractions during the progress of the reaction and during the so‐called ambient temperature “maturing” of the resin. The PMUF resin also showed both similar and different fractions present during manufacturing and during short term ageing at ambient temperature. While similarities in recurrent fractions and in trends are common to all the three different formulations, differences between them are also clearly observed. A major proportion of the reaction of some of the aminoplastic resins examined also occurs on ageing (i.e.“maturing” of the resin at ambient temperature), this appearing to be an essential phase of the resin preparation process. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 4842–4855, 2006