Chemical Facilities Research Articles

A framework of sensitivity analysis for the performance assessment of safety barriers impacted by NaTech accidents

Hazardous Natural events can cascade into Technological accidental scenarios (so called NaTech accidents). The occurrence of these accidents can degrade the performance of the preventive and mitigative safety barriers installed in the technological plants. Such performance degradation is typically assessed by expert judgement, without considering the effect of the magnitude of the natural hazard, nor its increasing frequency of occurrence in view of climate change. In this work, a novel sensitivity analysis framework is developed to identify the safety barriers whose performance degradation is most critical and thus needs careful modeling for realistic risk assessment. The framework is based on the calculation of a set of sensitivity measures, namely the Beta, the Conditional Value at Risk (CVaR) and the Value of Information (VoI),●and their use to prioritize the safety barriers with respect to the need of:●accounting for performance degradation during an accidental scenario;●planning investments for further characterization of the safety barrier performance.An application is shown with respect to a case study of literature that consists of a chemical facility equipped with five safety barriers (of three different types, active, passive and procedural). NaTech scenarios can occur, triggered by floods and earthquakes. The results obtained with the Beta measure indicate that two-out-of-five barriers (one active and one passive) deserve accurate modelling of the performance degradation due to natural events. An additional outcome is that in the case study considered, both CVaR and VoI rank the passive barrier as the most effective in mitigating the scenarios escalation: therefore, this barrier is the one for which the decision maker could decide to invest resources for improving the characterization of its performance to obtain a more realistic assessment of the risk.

Data-driven modeling of H2 solubility in hydrocarbons using white-box approaches

As a result of technological advancements, reliable calculation of hydrogen (H2) solubility in diverse hydrocarbons is now required for the design and efficient operation of processes in chemical and petroleum processing facilities. The accuracy of equations of state (EOSs) in estimating H2 solubility is restricted, particularly in high-pressure or/and high-temperature conditions, which could result in energy loss and/or potential safety and environmental problem. Two strong machine learning techniques for building advanced correlation were used to evaluate H2 solubility in hydrocarbons in this study which were Group method of data handling (GMDH) and genetic programming (GP). For that purpose, 1332 datasets from experimental results of H2 solubility in 32 distinct hydrocarbons were collected from 68 various systems throughout a wide range of operating temperatures from 98 K to 701 K and pressures from 0.101325 MPa to 78.45 MPa. Hydrocarbons from two distinct classes include alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. Hydrocarbons have a molecular mass range of 28.054–647.2 g/mol, which corresponds to a carbon number of 2–46. Solvent molecular weight, critical pressure, and critical temperature, as well as pressure and temperature operational parameters, were used to create the features. With a regression coefficient (R2) which was equal to 0.986 and root mean square error (RMSE) which was 0.0132, the GP modeling approach estimated experimental solubility data more accurately than the GMDH approach. Operating pressure, followed by molecular weight of hydrocarbon solvents and temperature, had the greatest influence on estimation H2 solubility, according to sensitivity analysis. The GP model shown in this paper is a reliable development that may be used in the chemical and petroleum sectors as a reliable and effective estimator for H2 solubility in diverse hydrocarbons.

Optimization of gas detectors placement in complex industrial layouts based on CFD simulations

The optimal placement of hazardous gas detectors plays a relevant role in the prevention and mitigation of critical scenarios among chemical and process facilities. However, the operative performance of gas sensors networks is affected by the complexity of process facilities layout and sensors’ reliability. Moreover, economic constraints may bias the selection of the optimal number of sensors. The present work deals with the development of a methodology to support the selection and positioning of hazardous gas detectors in complex industrial layouts. The methodology exploited preliminary computational fluid dynamics (CFD) simulations to quantify the extension of hazardous gas clouds. A comparison among gas dispersion studies based on CFD and integral models was also carried out to demonstrate the limitation of adopting simplified analyses on the evaluation of gas dispersion hazards. The optimization methodology was then developed based on a twofold algorithm. The first algorithm aimed at determining the optimal sensors positions, seeking for the locations that maximize the detection frequency for a given number of sensors. The second algorithm adopted a multicriteria decision analysis based on the reference point method to find the optimal economic number of sensors and, thus, the most cost-effective strategy. A case study located in a real gasoline storage plant was analysed. The results demonstrated the advantage of adopting a sound quantitative evaluation of the number of detectors integrating economic and risk-based evaluations.

Probabilistic assessment aimed at the evaluation of escalating scenarios in process facilities combining safety and security barriers

Over the last twenty years, physical security threats to chemical and process facilities became more and more relevant all around the world. Intentional attacks have the potential to trigger severe escalation scenarios. However, in literature there is not a comprehensive and consolidated approach to account for the effect of physical protection systems (PPSs) in the framework of conventional process safety analysis and to consider their contribution with the prevention and mitigation actions of safety barriers. Therefore, the aim of the present work is to develop a probabilistic study dedicated to chemical and process facilities accounting for the combined contribution of safety barriers and PPSs in the prevention of cascading events triggered by security attacks. The quantitative performance of PPSs was characterized in terms of availably and effectiveness, building a specific data repository. The performance data obtained were included into event trees, in which safety barriers were implemented. The analysis of an industrial case study demonstrated the potentially of the methodology in analysing complex escalation scenarios, identifying the critical PPS elements and, at the same time, showing the critical role of safety barriers in protecting against scenarios triggered by security attacks.

Certainty of Punishment versus Severity of Punishment

<h3>Abstract</h3> According to the standard enforcement model, the key deterrence components are punishment certainty and severity. While theory predicts the relative efficacy of certainty versus severity, empirical and experimental evidence are mixed. Our study represents the first to systematically compare the effects of certainty and severity in the environmental protection context. Our empirics examine wastewater discharged by chemical manufacturing facilities permitted under the Clean Water Act. We find that, when enforcement certainty and severity are high, both components effectively deter pollution, with certainty more effective. In contrast, certainty and severity increases prove counter-productive when certainty and severity are low.

The pursuit of net-positive sustainability for industrial decarbonization with hybrid energy systems

Signatories of the Paris Agreement are set to miss their climate targets. The net-zero pledges announced to date across many countries and private industries are insufficient to achieve carbon neutrality, which requires implementation of far-reaching and significantly scaled-up climate-positive actions. All technology options that pertain to deep decarbonization and carbon removal must be part of the mitigation portfolio. Transformative action plans must be established in which every individual/organization around the globe is an actor of changes in relation to net-zero goals. Such plans must involve governmental policy support but also encourage voluntary efforts to boost innovations, investments, initiatives, and behavioral changes. Recognition of these efforts is made quantifiable with the concepts of carbon handprint and net positivity. This paper presents a carbon handprint perspective on characterizing the environmental benefits of hybrid energy systems (HESs)—a widely applicable solution to cleaner production leveraging the capabilities of a portfolio of low-carbon energy sources—that provide heat and electricity to industrial processes. First, the carbon handprint and net positivity concepts and their calculation approach are introduced. The state of the art of HES-enabled industrial cogeneration is then surveyed, and the greenhouse gas emission intensities of different energy sources that power HESs are compared. Next, drawing on a case study about a US chemical facility's voluntary initiative to explore replacing its fossil fuel–based cogeneration infrastructure with a clean energy–generated HES, several technically viable scenarios are evaluated—especially those with small modular nuclear reactors—to illustrate how the positive-thinking handprint approach helps encourage and inform the search for widespread influence pathways in pursuit of net-positive sustainability . Finally, current knowledge gaps in the case study are identified, and opportunities to scale up the proposed handprint-based analysis are outlined with consideration of an expanded role of HESs in fulfilling climate objectives. It is envisioned that like-minded decision makers in the industry sector and beyond will adopt this perspective and act synergistically to enhance their environmental stewardship through voluntary actions and make greater contributions to the planet's climate future. • Environmental benefits of hybrid energy systems are assessed using carbon handprint. • Hybrid energy systems produce cleaner heat and power for industrial processes. • Carbon handprint measures impacts of voluntary climate-positive actions. • Exemplary handprint-based net-positivity analysis is performed on a case study. • Opportunities for nuclear and renewable energy are highlighted in the case study.

An Intelligent Real-Time Edge Processing Maintenance System for Industrial Manufacturing, Control, and Diagnostic

This paper presents an artificial intelligence (AI) based edge processing real-time maintenance system for the purposes of industrial manufacturing control and diagnostics. The system is evaluated in a soybean processing manufacturing facility to identify abnormalities and possible breakdown situations, prevent damage, reduce maintenance costs, and increase production productivity. The system can be used in any other manufacturing or chemical processing facility that make use of motors rotating equipment in different process phases. The system combines condition monitoring, fault detection, and diagnosis using machine learning (ML) and deep learning (DL) algorithms. These algorithms are used with data resulting from the continuous monitoring of relevant production equipment and motor parameters, such as temperature, vibration, sound/noise, and current/voltage. The condition monitoring integrates intelligent Industrial Internet of Things (IIoT) devices with multiple sensors combined with AI-based techniques and edge processing. This is done to identify the parameter modifications and distinctive patterns that occur before a failure and predict forthcoming failure modes before they arise. The data from production equipment/motors is collected wirelessly using different communication protocols - such as Bluetooth low energy (BLE), Long range wide area network (LoRaWAN), and Wi-Fi - and aggregated into an edge computing processing unit via several gateways. The AI-based algorithms are embedded in the processing unit at the edge, allowing the prediction and intelligent control of the production equipment/motor parameters. IIoT devices for environmental sensing, vibration, temperature monitoring, and sound/ultrasound detection are used with embedded signal processing that runs on an ARM Cortex-M4 microcontroller. These devices are connected through either wired or wireless protocols. The system described addresses the components necessary for implementing the predictive maintenance (PdM) strategy in soybean industrial processing manufacturing environments. Additionally, it includes new elements that broaden the possibilities for prescriptive maintenance (PsM) developments to be made. The type of ML or DL techniques and algorithms used in maintenance modeling is dictated by the application and available data. The approach presented combines multiple data sources that improve the accuracy of condition monitoring and prediction. DL methods further increase the accuracy and require interpretable and efficient methods as well as the availability of significant amounts of (labeled) data.

Health status analysis of the population living in the zone of protective measures of a chemical weapons destruction facility, during the conversion period

Introduction. One of the tasks to be solved in the framework of the of the “Destruction of chemical weapons stockpiles in the Russian Federation” Federal Targeted Program was the social and hygienic monitoring (control) over the health of the population living in the zones of protective measures (ZPMs) of chemical weapons destruction facilities (CWDFs) as an integral part of state policy in the field of ensuring citizens’ guarantees for a favorable living environment and preservation of life and health. The purpose of the study is to determine the possible impact of the conversion of a chemical weapons destruction facility on the health of the population living in the zone of protective measures. Materials and methods. The article presents the results of the assessment, according to the statistical reporting forms, the health parameters of the population living in the zone of protective measures of the Maradykovsky CWDF located in the Kirov region. Results. Analysis of the medical and demographic parameters of the Orichi and Kotelnich Districts, Kirov Region, located in the ZPM zone of the Marazykovsky CWDF, in 2018, a decrease in the population compared to the previous year by 0.7% and 2.1%, respectively, was revealed. The birth rate proved to be lower compared to the weighted average birth rates over the period of 2010-2015, and the overall mortality rate proved to be higher than the birth rate, which was explained by the aging of the population and the regressive type of age structure. The prevalence and primary morbidity rates among the adult population were statistically significantly lower than the levels in the control groups (p &lt; 0.05), and the structure of prevalence and primary morbidity compared with the respective values for the control group. Comparison of the prevalence rates for certain classes of diseases between the populations living in the territory in focus and in other territories revealed no increase in the mass noninfectious morbidity threshold. Limitations. When studying the health indicators of the population living in the zone of protective measures of the chemical weapons destruction facility “Maradykovsky”, located in the Kirov region, an assessment of the statistical reporting materials of the departments of the MIAC of the Kirov region, data from reporting forms No. 12 “Information on the number of diseases registered in patients living in the service area of the medical institution” of the Kirov region was carried out. Conclusion. The study revealed no correlation the activities of the the Maradykovsky CWDF during the conversion period and the health status of the population living in the ZPM of the facility.

SK invests at Ireland pharmaceutical chemical facility

SK invests at Ireland pharmaceutical chemical facility

The U.S. Bioeconomy: Charting a Course for a Resilient and Competitive Future

The U.S. Bioeconomy: Charting a Course for a Resilient and Competitive Future

Stroitel'stvo i pusk Bereznikovskogo himicheskogo kombinata

The complex process of origin and formation of the largest chemical plant in the USSR in the territory of the Western Urals, the first and unparalleled enterprise in the Soviet industry, is considered. The difficulties in the construction of a new complex production, the role of foreign firms and specialists in the construction of complex chemical industry facilities, the hard work of Soviet pioneer builders and the importance of the created powerful infrastructure for the official formation in 1932 of the young socialist city of Berezniki, now the second largest and socio-economic potential city of the Perm region after the regional center of Perm, are shown.

New Chemical Weapons Destruction Technologies as the Key to Successful Completion of Chemical Weapons Disarmament Process

The fulfillment of international obligations of the Russian Federation under the Convention on the Prohibition of the Development, Production, Stockpiling and Use of Chemical Weapons and on Their Destruction required the development and the creation of safe innovative industrial technologies and facilities for the destruction of chemical weapons. The purpose of this work is to give brief characteristics of the technologies for the destruction of chemical weapons developed and commercially implemented at various facilities. The destruction of chemical weapons was carried out at seven facilities specially designed and built for this purpose. These facilities were located in six regions of the country. The choice of technologies for the destruction (utilization) of chemical weapons was carried out on a competitive basis in the period from 1992 to 1995. The priority was given to the so-called two-stage technology. At the first stage, the toxic chemicals were extracted from the munitions or the containers and detoxified chemically in «soft» controlled conditions. Then the empty munitions were degassed. At the second stage, thermal neutralization or bituminization of the reaction masses was carried out with their subsequent burial. The two-stage technology for the destruction of mustard and mustard-lewisite mixtures was based on the interaction of toxic chemicals with an 80 ± 5% aqueous solution of monoethanolamine, which was supplied into the reactor at a temperature of 60–80 °C in the ratio toxic chemical: degassing formulation - 1:1.2 according to mass. The detoxification process was considered completed if the content of toxic chemicals in the reaction mass did not exceed 3,2×10-3%. To destroy lewisite, a «short scheme» in a jet-type reactor was implemented. The mixing of the initial reagents, lewisite and 20% alkali solution, was carried out using a nozzle of a special design, where lewisite was swirled with a special device (swirler) and supplied into the reactor. The first stage of the destruction of V-gases was carried out in munitions cases. The munitions were considered as chemical reactors. The process of detoxification of V-gases was considered completed when the residual content of toxic chemicals was at the level of 5×10-4%, and the reaction mass was delivered for thermal neutralization (second stage). In total, 39966,588 tons of toxic chemicals were destroyed. The whole number of destroyed containers with agents and chemical munitions amounted to 4,158,456 units. On September 27, 2017, the last chemical weapon was destroyed at the Kizner chemical weapons destruction facility, thus completing the process of complete destruction of chemical weapons stockpiles in the Russian Federation.

Reducing the Potential Scope of Chemical Catastrophes in the US Chemical Industry: Evidence From the EPA's Risk Management Planning Program.

Available information suggests that some chemical facilities are reducing the potential for catastrophic chemical releases to nearby communities. However, the actual extent and nature of any technological changes are not well documented, and the pace of change is slow at best. Moreover, major federal programs do not routinely investigate industry practices and communicate lessons learned, thereby missing important opportunities to generate knowledge of solutions that reduce populations at risk.

Physics-based Demand Model and Fragility Functions of Industrial Tanks under Blast Loading

Blast hazards represent a serious threat to industrial facilities. Past explosion incidents highlight the severe consequences of such events. A probabilistic approach can help industries and designers mitigate the consequences of blast loading by better organizing industrial plants. In this paper, we propose a physics-based probabilistic demand model and formulate the reliability problem for industrial steel tanks under blast loading. Starting from a deterministic Single-Degree-of-Freedom (SDOF) model based on Donnell shallow-shell theory, we develop a correction term that improves the model accuracy due to the simplified representation of the SDOF model. We use Bayesian inference to estimate the unknown model parameters in the correction term and model error, combining predictions from the SDOF model with experimental data and any prior information. To illustrate, we estimate the reliability of an example cylindrical steel tanks subject to blast loading considering three damage levels. The reliability analysis yields a set of fragility curves that represent the conditional probability of the bending failure of the tank given a scaled distance, as the load intensity measure. Then, as an example, we use the developed fragility functions to estimate the reliability of a chemical industrial facility considering different explosion scenarios.

Numerical Simulations of a Postulated Methanol Pool Fire Scenario in a Ventilated Enclosure Using a Coupled FVM-FEM Approach

Numerical investigations have been carried out for a postulated enclosure fire scenario instigated due to methanol pool ignition in a chemical cleaning facility. The pool fire under consideration is radiation-dominated and poses a risk to the nearby objects if appropriate safety requirements are not met. The objective of the current study was to numerically evaluate the postulated fire scenario and provide safety recommendations to prevent/minimize the hazard. To do this, the fire scenario was first modeled using the finite volume method (FVM) based solver to predict the fire characteristics and the resulting changes inside the enclosure. The FDS predicted temperatures were then used as input boundary conditions to conduct a three-dimensional heat transfer analysis using the finite element method (FEM). The coupled FVM–FEM simulation approach enabled detailed three-dimensional conjugate heat transfer analysis. The proposed FVM–FEM coupled approach to analyze the fire dynamics and heat transfer will be helpful to safety engineers in carrying out a more robust and reliable fire risk assessment.

Modeling and Controller Design for Temperature Control of Heat Exchangers in Power Plants

A heat exchanger is a device that transfers heat from one place to another. Because it can withstand a wide range of temperature and pressure, the power plant heat exchanger is widely employed in chemical and petroleum facilities. A heat exchanger is a device that transfers heat from one place to another. Plant with strong nonlinearity and low dynamics; as a result it is tough to model and manage its dynamics. There are two types of heat exchanger models in this study. For selecting an appropriate model, controllers are used. Controller. (Physical model) is the first model. Generated from genuine heat exchanger plant parameters Second, there's Dead Time plus a Second Order (SOPDT model)This is obtained from the heat exchanger's response. While The controllers are made up of fuzzy proportional controllers. Proportional integral (FPD) controller and derivative (FPD) controller applied to the model as a derivative (PID) controller Their comments are compared to those of the others. Keywords Power Plant Heat Exchanger, Modelling, Fuzzy Control.

Demonstration of VOC Fenceline Sensors and Canister Grab Sampling near Chemical Facilities in Louisville, Kentucky.

Experimental fenceline sensor pods (SPods) fitted with 30 s duration canister grab sampling (CGS) systems were deployed at a site near chemical facilities in Louisville, KY, from 4 June 2018 to 5 January 2020. The objective of the study was to better understand lower cost 10.6 eV photoionization detector (PID)-based volatile organic compound (VOC) sensors and investigate their utility for near-source emissions detection applications. Prototype SPods containing PID sensor elements from two different manufacturers yielded between 78% and 86% valid data over the study, producing a dataset of over 120,000 collocated pair fenceline measurements averaged into 5-min datapoints. Ten-second time-resolved SPod data from an elevated fenceline sensor signal day are presented, illustrating source emission detections from the direction of a facility 500 m west of the monitoring site. An SPod-triggered CGS acquired in the emission plume on this day contained elevated concentrations of 1,3-butadiene and cyclohexane (36 parts per billion by volume (ppbv) and 637 ppbv, respectively), compounds known to be emitted by this facility. Elevated concentrations of these compounds were observed in a subset of the 61 manual and triggered CGS grab samples acquired during the study, with winds from the west. Using novel wind-resolved visualization and normalization approaches described herein, the collocated pair SPod datasets exhibited similarity in emission source signature. With winds from the west, approximately 50% of SPod readings were above our defined theoretical detection limit indicating persistent measurable VOC signal at this site. Overall, this 19-month study demonstrated reasonable prototype SPod operational performance indicating that improved commercial forms of lower cost PID sensors could be useful for select VOC fenceline monitoring applications.

Evaluation of a Gene–Environment Interaction of PON1 and Low-Level Nerve Agent Exposure with Gulf War Illness: A Prevalence Case–Control Study Drawn from the U.S. Military Health Survey’s National Population Sample

Background:Consensus on the etiology of 1991 Gulf War illness (GWI) has been limited by lack of objective individual-level environmental exposure information and assumed recall bias.Objectives:We investigated a prestated hypothesis of the association of GWI with a gene–environment (GxE) interaction of the paraoxonase-1 (PON1) Q192R polymorphism and low-level nerve agent exposure.Methods:A prevalence sample of 508 GWI cases and 508 nonpaired controls was drawn from the 8,020 participants in the U.S. Military Health Survey, a representative sample survey of military veterans who served during the Gulf War. The PON1 Q192R genotype was measured by real-time polymerase chain reaction (RT-PCR), and the serum Q and R isoenzyme activity levels were measured with PON1-specific substrates. Low-level nerve agent exposure was estimated by survey questions on having heard nerve agent alarms during deployment.Results:The GxE interaction of the Q192R genotype and hearing alarms was strongly associated with GWI on both the multiplicative [prevalence odds ratio (POR) of the ; 95% confidence interval (CI): 1.20, 9.72] and additive (synergy ; 95% CI: 1.82, 12.19) scales, adjusted for measured confounders. The Q192R genotype and the alarms variable were independent (adjusted POR in the ; 95% CI: 0.81, 1.73; ), and the associations of GWI with the number of R alleles and quartiles of Q isoenzyme were monotonic. The adjusted relative excess risk due to interaction (aRERI) was 7.69 (95% CI: 2.71, 19.13). Substituting Q isoenzyme activity for the genotype in the analyses corroborated the findings. Sensitivity analyses suggested that recall bias had forced the estimate of the GxE interaction toward the null and that unmeasured confounding is unlikely to account for the findings. We found a GxE interaction involving the Q-correlated PON1 diazoxonase activity and a weak possible GxE involving the Khamisiyah plume model, but none involving the PON1 R isoenzyme activity, arylesterase activity, paraoxonase activity, butyrylcholinesterase genotypes or enzyme activity, or pyridostigmine.Discussion:Given gene–environment independence and monotonicity, the unconfounded supports a mechanistic interaction. Together with the direct evidence of exposure to fallout from bombing of chemical weapon storage facilities and the extensive toxicologic evidence of biochemical protection from organophosphates by the Q isoenzyme, the findings provide strong evidence for an etiologic role of low-level nerve agent in GWI. https://doi.org/10.1289/EHP9009

Attractiveness Assessment of Chemical Facilities Operating in Critical Areas: Development of a Semi-Quantitative Methodology Tailored to the Maghreb Context

Attractiveness Assessment of Chemical Facilities Operating in Critical Areas: Development of a Semi-Quantitative Methodology Tailored to the Maghreb Context

Application of data mining to minimize fire-induced domino effect risks.

Data mining (DM) has been applied in many advanced science and technology fields, but it has still not been used for domino effect risk management to explore minimum risk scenarios. This work investigates the feasibility of DM in minimizing the risk of fire-induced domino effects in chemical processing facilities. Based on DM, an evidential failure mode and effects analysis (E-FMEA), which could bridge chemical facilities' operational reliability and domino effect risk, is combined with fault tree analysis (FTA) for the occurrence risk modeling of loss of containment (LOC) event of chemical facilities, which is often the triggering point of fire-induced domino effects. Industry specific data such as reliability data, inspection records, and maintenance records are of great value to model the potential occurrence criticality of LOC. The data are used to characterize the LOC risk priority number (RPN) of chemical facilities through FTA and E-FMEA, search and statistics rules are proposed to mine inspection records to assess LOC risk factors. According to the RPN scores of facilities, inherent safety strategies to minimize risk via inventory control are proposed, and their effectiveness is tested using a well-known probit model. In this way, the approach proposes a unit-specific evidence-based risk minimization strategy for fire-induced domino effects. A case study demonstrates the capability of DM in the risk minimization of fire-induced domino effects.