Chemical Processing Plant Research Articles

Exploring the Potential of High Entropy Alloys: A Comprehensive Review on Microstructure, Properties and Applications: Part I

Over the past few years, there has been a notable surge in research interest surrounding high entropy alloys (HEAs) owing to their exceptional properties. Unlike conventional alloys, HEAs consist of five or more principal elements, which offer endless possibilities for developing new alloy systems. HEAs exhibit a high concentration of mixing elements, resulting in high disorderliness of the atomic structure within the material, known as high entropy. This unique nature provides HEAs with desirable properties, including excellent mechanical and physical properties at elevated temperatures, making them ideal for high-temperature applications like cryogenic engines and gas turbines. Moreover, HEAs have shown remarkable corrosion resistance, positioning them as viable options for applications in demanding environments such as marine settings, oil and gas pipelines and chemical processing plants. This comprehensive review paper analyses recent studies on various HEAs. Part I introduces HEAs and describes their synthesis, microstructure, hardness and strength properties.

Chemical plant optimization layout based on the domino hazard index considering the fixed hazard unit outside the available area

The layout of chemical plants is a significant step to promote the safe and efficient operation of a chemical factory. In the actual production process, the chemical process plant layout may be affected by immovable hazards outside the available layout area. In this paper, the external hazard unit is arranged as one of the chemical process plants. The domino effect potential of all chemical process plants is quantified by using the classical domino hazard index (DHI). A layout optimization model considering the effects of external hazard units has been proposed to obtain the optimal plant layout from economic and safety aspects. The feasible layout solutions are obtained under the orientation, boundary and non-overlapping constraints by the non-dominated sorting genetic algorithm II (NSGA-Ⅱ). The proposed model is further analyzed through a hypothetical case of three storage tanks outside the acrylic acid (AA) production plant layout area. The results show that the total cost range for the optimal solution is 650348–1365443 $, with corresponding DHI values ranging from 29 to 129. The plant with larger DHI value will be arranged to far away from the external hazard units to obtain a safer layout. This work can provide rational layout solutions for the layout designers to make different decisions according to the actual situation.

Partial derivative-based dynamic sensitivity analysis expression for non-linear auto regressive with exogenous (NARX) model[sbnd]case studies on distillation columns and model's interpretation investigation

Constructing the reliable dynamic sensitivity profile for the output variable using the machine learning model is a challenging task; however, the dynamic sensitivity trends are helpful to understand the impact of the input variables on the system's performance. In this paper, we have derived the partial-derivative approach-based sensitivity analysis expression for the non-linear auto regressive with exogenous (NARX) model for the first time. The engineering systems-based case studies, i.e., two distillation columns with five and ten stages, respectively are taken which are commonly found in the chemical processing plants. Two output variables, i.e., liquid composition in tray 2 and tray 4 (Y2 and Y4) of a five-stage distillation column, and liquid composition in tray 7 (Y7) of a ten-stage (higher) distillation column are modelled by NARX with respect to time, feed concentration (Xf) and feed flow rate (Lf). The dynamic sensitivity profiles of the output variables with respect to Xf and Lf for the two distillation columns are plotted by the derived partial derivative-based sensitivity expression on the NARX model. Furthermore, the forward difference method of sensitivity analysis (first principle method) is also applied on the ordinary differential equations of the distillation columns to compute the sensitivity values of the output variables. A good agreement in the dynamic sensitivity values of the output variables with respect to the input variables is found for the two sensitivity analysis techniques thereby demonstrating the effectiveness of the partial-derivative approach for the improved NARX's interpretability performance. This research presents the explicit partial-derivative based sensitivity analysis expression for the NARX model which can be utilised for time-series applications and can provide the insights about the model's interpretation performance.

Predicting possible recommendations related to causes and consequences in the HAZOP study worksheet using natural language processing and machine learning: BERT, clustering, and classification

A set of recommendations is one of the most valuable outputs of the hazard and operability (HAZOP) study. The HAZOP study team provides recommendations when deficiencies are detected in the chemical process plant. These deficiencies can cause chemical process accidents and operability issues. This study employed a data-driven approach using natural language processing (NLP) and machine learning (ML) to predict potential recommendations based on causes and consequences. The dataset had no label; thus, clustering was used to label it. Firstly, bidirectional encoder representations from transformers (BERT) converted recommendation sentences into vectors. Secondly, uniform manifold approximation and projection (UMAP) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) were utilized to determine recommendation categories and label the dataset. Then, BERT was used to convert causes and consequences into vectors. Finally, a multi-layer perceptron (MLP) classifier was employed to predict possible recommendations based on causes and consequences. The class imbalance problem was handled by random over-sampling. The prediction accuracy of possible recommendations based on causes and consequences equals 93.7% and 89.5%, respectively. As a result of predicting potential recommendations utilizing causes and consequences, major recommendations will not be overlooked during the HAZOP study. This can further expand NLP and ML applications in HAZOP study automation.

Evaluation of Student Perceived Learning Effectiveness and Motivation for a Capstone Design Web-based Virtual Reality Activity

This study reports on the second implementation of a chemical processing plant web-based VR for a fourth-year chemical engineering capstone design course. The web-based VR provides a high fidelity representation of an ampicillin processing facility. To examine the effect of the web-based VR on student motivation and perceived learning effectiveness, a broken plant scenario was developed, and the class was divided into two groups: paper based, and web-based VR. Students completed the scenario in teams, either as a paper-based activity or using the web-based VR. This was followed by completion of an individual tutorial assignment and a survey. Survey findings indicated that students in the web-based VR group had a significantly higher positive response for motivation and perceived learning effectiveness compared to students in the paper-based group. However, students in the paper group scored higher on the individual follow-up assignment. Possible factors contributing to the results are discussed.

Assessing the environmental risk of fire and explosion events associated with chemical process routes

The development of inherently environmentally friendly chemical process plants is one approach to reduce or avoid potential harmful environmental consequences that could occur due to chemical plant accidents. This can be achieved by selecting the chemical process route involving the least environmental risk, during preliminary design stage. To assist the route selection process a risk-based index called Inherent Environmental Risk Index for Fires and Explosions (F&E-IERI) is proposed. The F&E-IERI assesses and ranks chemical process routes considering environmental impacts caused by accidental fires and explosions that could occur in a chemical plant. This index considers the environmental impact magnitude, fire or explosion event occurrence frequency, and the environmental element vulnerability. A higher index associated with the route indicates a higher environmental risk. The proposed index has been applied in four possible routes to produce acetone. The p-diisopropyl benzene oxidation process route to produce acetone showed the lowest F&E-IERI value indicating the route having the lowest environmental risk.

Power profile analysis of criticality accidents involving fissile solution boiling with considering evaporation

ABSTRACT The total fission energy released in a criticality accident involving fissile solution boiling tends to be high because the relatively high fission power continues during boiling. Simulating fission power change correctly during boiling seems essential to estimate the total fission energy. Fission power during boiling changes depending on fissile concentration and volume as the solution evaporates. In this study, we investigated the effect of concentration and volume change on estimated total fission energy for a long time of boiling. We introduced a model calculating the evaporation of fissile solution into the modified quasi-steady-state method to simulate power change during boiling. Three CRAC experiments and the Idaho Chemical Processing Plant (ICPP) criticality accident in 1959 were analyzed. As a result, the calculated energy considering concentration and volume change during boiling reproduced the measured energy well.

A licensing plan for coupling nuclear–renewable hybrid energy systems

South Africa committed to reducing greenhouse gas emissions by 34% below "Business As Usual" levels by 2020 and 42% by 2025 at the Copenhagen climate change conference. However, global energy demand was projected to rise due to population and industrial growth, with energy usage expected to increase by 13% through 2040. In South Africa, a steady increase in electricity demand prompted the government to implement a new growth path (NGP), which prioritized job creation in all economic policies, including energy. To address the growing energy demand, an alternative energy source for combined heat and power was necessary, particularly for synthetic fuel production. The coupling of a high temperature reactor (HTR) with a chemical process plant could improve carbon and thermal efficiency, as seen in international organizations like the ARCHER (Advanced High-Temperature Reactors for Cogeneration of Heat and Electricity R&D) project. HTRs are still in their conceptual stages andaresuitable candidate forcouplingto an energy-intensiveindustrythat require process heat with sufficiently hightemperatures,with potential uses for NRHES (Nuclear-Renewable Hybrid Energy Systems). The South African team aimed to model the SASOL process, assess its financial viability, and develop a licensing plan.

Accidents in Chemical & Process Industries of Bangladesh: Awareness Among People Working in Chemical & Process Plants

Chemical usage has steadily expanded in tandem with economic expansion, resulting in an increase in chemical accidents. Chemical incidents endanger lives and can cause a slew of health issues for those who live near chemical factories. The purpose of this study was to evaluate the knowledge of chemical accidents in chemical sector and awareness about the risk they pose among those working in chemical facilities and process plants. The research was conducted through survey which was composed of questionnaire regarding chemical safety and chemical accident in different chemical sectors participated by 120 people working in different chemical plants. Although the process safety has not mentioned in any kind of safety rules and regulations or acts the employee should have received the safety training to mitigate hazardous situation. The study shows that there was a lack of awareness and negligence of using PPE in most of the employees. A large number of respondents were doubtful of their plants following safety rules and regulations and also there was a negligence of following rules and regulations among the employees. Though there is eagerness to take safety training but most of the plants didn’t provide proper training to their employees. Journal of Engineering Science 14(1), 2023, 53-63

NO2 emissions from oil refineries in the Mississippi Delta

Of the >17,943 thousand barrels per calendar day (bbl/d) of oil refining capacity located in the US, the Petroleum Administration for Defense District 3 (PADD-3) region has the largest number of refineries and accounts for >53 % (or 9607 tbbl/d) of all US oil refining capacity. Processing facilities in this area are mainly located on the Gulf of Mexico coast in Texas and Louisiana. This study selected a sub-region for analysis within the Mississippi River delta in the state of Louisiana between the cities of New Orleans and Baton Rouge. This region is characterized by intensive industrial activity connected with oil refining and related activities. The TROPOspheric Monitoring Instrument (TROPOMI) detected highly localized NO2 vertical column densities (VCDs) over the two largest US refineries in Baton Rouge (503,000 bbl/d) and Garyville (578,000 bbl/d). TROPOMI NO2 VCD over these stations were 100 μmol/m2 and 80 μmol/m2, respectively. A high correlation coefficient (r = 0.65, p < 0.05) was also found between TROPOMI NO2 and population density. Data from the National Emissions Inventory (NEI) showed high NOx emissions from refineries and other industries including coal-fired power generation, chemical, and aluminum processing plants. The results of the NO2 analysis are of practical interest for a comparative assessment of air pollution, as well as for the exchange of best practices in the field of low-waste fuel combustion technologies.

Multi-objective optimization of chemical process plant layout considering economy and inherent safety

Economy and safety are important factors for the layout design of chemical process plants. However, safety factors are often translated into economic indicators as a part of the objective function for layout optimization in previous studies. This may result in safety performance of chemical process plants does not meet the actual production requirements. In this paper, a multi-objective optimization method for chemical process plant layout is proposed to obtain series of solutions that can achieve the trade-off between economy and safety. The economic objective function is characterized by the total capital cost, including land cost, pipe cost, protection device cost and expected property loss from accidents. The safety objective function involves inherent safety which is evaluated by the Comprehensive Inherent Safety Index (CISI). The proposed multi-objective optimization model solved by non-dominated sorting genetic algorithm II (NSGA-II) is applied for layout optimization of an acrylic acid production plant. The results show a strong trade-off between competing economic and safety objectives according to the Pareto curve. The optimal solution ranges from 236,099 $ to 379,622 $ for total cost, corresponding to 87.7–132 for CISI. Safety will be improved significantly in the low-safety layout scheme when the capital investment increases slightly.

Detecting Plant-Wide Oscillation Propagation Effects of Disturbances and Faults in a Chemical Process Plant Using Network Topology of Variance Decompositions

This work demonstrates for the first time the application of network topology of variance decompositions in analyzing the connectedness of chemical plant process variable oscillations arising from disturbances and faults. Specifically, the time-based connectedness and frequency-based connectedness of variables can be used to compute the net pairwise dynamic connectedness (NPDC), which originated as a volatility spillover index for financial markets studies in the field of econometrics. This work used the anomaly-detection benchmark Tennessee-Eastman chemical process (TEP) dataset, which consists of 41 measured variables and 11 manipulated variables subjected to various faulty operating conditions. The data analytics was performed using key functions from the R-package ‘ConnectednessApproach’ that implements connectedness computations based on time and frequency. The NPDC coefficient matrices were then transformed into network adjacency matrices for the rendering of the network topology of connectedness for TEP. The resulting network topologies allow a comprehensive analysis of oscillation effects across all plant-measured and manipulated variables. Analyzing the directed connectedness of the system dynamics at short-range, mid-range, and long-range frequencies showed how the oscillation effects of disturbances and faults propagate and dissipate in the short-term, mid-term, and long-term periods.

Dynamic response characteristics and structural optimization of isolation seal under the impact of gas explosion

Either in the chemical process plants or in the underground infrastructures, the isolation seal is regularly used to separate the working sections and inactive sections, or to isolate the possible explosion sites in order to avoid any domino effects. Due to differences in accumulation space or ignition point locations, pressure on the seal can vary when an explosion occurs. Thus, the safety and reliability of seals are crucial to maintaining safety in process industry. This paper focuses on seals constructed with concrete and loess materials, and examines the dynamic response characteristics of the gas explosion wave on the seal through sample experiments and numerical simulation metods. The study proposes an optimized design for the explosion-proof structure of the wedge-shaped and spherical seal, which can provide a technical basis for the explosion-proof and anti-explosion measures of various sealed walls. These research findings can also serve as a basis for improving the construction quality of seals.

Safety analysis and risk assessment of a Micro-GtL Plant

Laboratory hydrogen generators, medical oxygen, and micro-breweries are examples of modular and micro technologies that are commercial successes. Researchers, patients, and unskilled workers operate these facilities but more complex processes require highly qualified personnel to ensure they operate safely. Modular-micro processes in isolated locations meet economic objectives when operated remotely thereby minimizing labor costs. Mitigating the risk requires a comprehensive hazards analysis with advanced control systems particularly for explosive and toxic compounds. Here, we propose a method called Failure Mode Risk Decision (FMRD) to review the inherent hazards of a micro-refinery unit (MRU) that converts flared and wasted natural gas to long chain hydrocarbons. This approach combines the Process Flow Failure Mode (PFFM) methodology as a systematic and reliable technique with a novel numerical risk assessment to improve the analytical evaluation of hazardous conditions. The objective is to combine causes and consequences in a single metric, where scaled probability of evident causes and severity of consequences are used to derive a risk level measure. With the proposed metric, the magnitude of a potential hazard is directly correlated with the risk level. This mechanism identifies extra risk scenarios compared to the classical hazard analysis method and provides a straightforward comprehensive numerical assessment to represent the inherent and residual risks to facilitate justifying the hazardous scenarios. Accordingly, we design a safety loop and supply all the required facilities to remove the potential risks at the process plant. Not only the proposed methodology clarifies the risks of the MRU presented in this study, but can be extended to review the hazards of other chemical process plants.

HVA_CPS proposal: a process for hazardous vulnerability analysis in distributed cyber-physical systems

Society is increasingly dependent upon the use of distributed cyber-physical systems (CPSs), such as energy networks, chemical processing plants and transport systems. Such CPSs typically have multiple layers of protection to prevent harm to people or the CPS. However, if both the control and protection systems are vulnerable to cyber-attacks, an attack may cause CPS damage or breaches of safety. Such weaknesses in the combined control and protection system are described here as hazardous vulnerabilities (HVs). Providing assurance that a complex CPS has no HVs requires a rigorous process that first identifies potential hazard scenarios and then searches for possible ways that a cyber-attacker could cause them. This article identifies the attributes that a rigorous hazardous vulnerability analysis (HVA) process would require and compares them against related works. None fully meet the requirements for rigour. A solution is proposed, HVA_CPS, which does have the required attributes. HVA_CPS applies a novel combination of two existing analysis techniques: control signal analysis and attack path analysis. The former identifies control actions that lead to hazards, known as hazardous control actions (HCAs); the latter models the system and searches the model for sequences of attack steps that can cause the HCAs. Both analysis techniques have previously been applied alone on different CPSs. The two techniques are integrated by extending the formalism for attack path analysis to capture HCAs. This converts the automated search for attack paths to a selected asset into an exhaustive search for HVs. The integration of the two techniques has been applied using HCAs from an actual CPS. To preserve confidentiality, the application of HVA_CPS is described on a notional electricity generator and its connection to the grid. The value of HVA_CPS is that it delivers rigorous analysis of HVs at system design stage, enabling assurance of their absence throughout the remaining system lifecycle.

Geometrical interpretation of Data Reconciliation and uncertainty reduction in Multi-Sensor systems

A data reconciliation and uncertainty reduction process, for multi-sensor systems such as power production plants, chemical processing plants, and many others, is considered. Methods of correcting original measurements to reduce their uncertainty, and to obtain more reliable information about the system, known as Data Validation and Reconciliation (DVR) or Process Data Reconciliation (PDR), have been developed in recent decades. These methods are present in literature as discussions on computer codes, statistical criteria, and details of specific industrial applications and implementations. This paper provides a mathematically accurate and intuitively understandable discussion on the scientific basis, benefits, limitations, and properties of the DVR/PDR technology. This is achieved through geometrical interpretation of this technology in a multidimensional metric space with metric tensor defined by the set of the original measurements. The effect of various factors on the methods’ output, are analyzed using graphical illustrations and analytical calculations.

Mutual impact of thermal radiations and temperature dependent thermal conductivity on non-Newtonian multiple flow regimes

The transfer of thermal energy is a specialized discipline of thermal engineering and hence own practical applications in an industrial thermal systems like refineries, air-conditioning plants, automotive engines, power plants, jet engines, refrigeration, food processing, and chemical processing plants to mention just a few. Owning such importance we have reported the collective impact of thermal radiations and temperature-dependent thermal conductivity for heat transfer in stagnation point magnetized Casson fluid flow field. The thermal flow regime of Casson fluid is manifested with a viscous dissipation effect as well. The flow equations are tackled numerically by the use of the shooting method. The outcomes are debated comparatively by considering various flow regimes namely (I) Thermal flow regime with thermal radiations (II) Thermal flow regime without thermal radiations (III) Heat flow with heat generation and (IV) Heat flow in the absence of heat generation. We believe that the present thermal comparative analysis will be helpful for the preceding studies which own thermal engineering standpoints for the transfer of thermal energy by means of fluid flow.

Guest Editorial: Facility Process Safety and Control

Temperature is a fundamental quantity relevant to almost all spheres of human endeavour. It is measured and controlled in a very wide range of applications, for example in combustion (engines, heating), heat treatment of high value components, healthcare (fever screening), meteorology and climate monitoring. Measurement and control of industrial process temperature in particular is key to maximising product quality, optimising energy efficiency, reducing waste and minimising carbon dioxide and other harmful emissions. It is also essential for ensuring safety; an accurate measure of temperature minimises the risk of operating outside the required process envelope (for example in nuclear power generation). There are several settings in which recent innovations have brought about significant improvements in thermometry, and these are described in this themed issue. Also discussed in this issue is the assessment and optimisation of safety in lithium-ion batteries, which is also a very important topic given the ubiquity of those devices, and testing for corrosion in chemical process plants, again a widespread issue of great relevance to many practitioners.

Energy Savings in the Heat Exchanger Network of an Oil Refinery Pre-Heat Train Unit Using a Path’s Combination at Different HRAT Values

Conservation of energy usage is essential in chemical process plants due to the expanded energy users and demands alongside the carry-on hike of energy prices. This study analyzed the performance of energy savings in a heat exchanger network (HEN). It is based on decreasing utility usage while increasing process-to-process heat exchange in HEN using a path combination approach at different heat recovery approach temperatures (HRATs). The approach generates different combined path options for heat shifting from utilities to exchangers in a HEN. In terms of cost targeting, the optimal HRAT in a HEN is determined for each path’s combination option. The study focused on the HEN of crude oil preheat trains. Shifting heat load between utilities implies adding and subtracting loads to and from exchangers in a HEN. Therefore, a minor retrofit to compensate for the heat transfer area is required for some HEN exchangers. The optimum HRAT corresponding to the lowest total cost was determined for each option and ranged between 8 °C and 14 °C. Moreover, two out of five options in HEN with low capital investment and a short payback period were found to be promising.

Emergency shutdowns of propylene production plants: Root cause analysis and availability modeling

Unplanned shutdown events in chemical process plants cause revenue losses due to production curtailments, repair costs, and harmful environmental and safety effects on plant personnel and the surrounding community. Several recent studies have used news articles and databases to perform statistical analyses and modeling of the causes and consequences of chemical process accidents, but these studies do not analyze the operating deviations that led to shutdowns. This work structures narratives about unplanned shutdown events into data, analyzes the causes of those events, and uses the collected data to model the failure probability of process plants. A case study comparing two propylene production technologies, steam cracking and propane dehydrogenation is developed. We use a set of 118 shutdown events reported in four sources across a five-year span for 7 propylene production plants. Using this data, the three leading causes for unplanned shutdowns in both propylene production technologies were determined to be: equipment trips/shutdowns, utility supply interruptions, and weather-related events. A power law process (PLP) for data from multiple repairable systems is used to model the failure behavior for both technologies. For the data collected, this procedure indicated an increasing failure intensity function for both technologies, with propane dehydrogenation increasing more significantly. In addition, both maximum likelihood estimates (MLEs) and confidence intervals for the PLP parameters are presented. Since these are repairable systems, an estimated inherent availability is determined for both technologies. Finally, recommendations that address how to reduce the occurrence of these events and mitigate their consequences are discussed.