Chemical Processing Plant Research Articles

A Hazard Index for Chemical Logistic Warehouses with Modified Flammability Rating by Machine Learning Methods

With the rapid development of chemical process plants worldwide, the safe storage of hazardous chemicals continues to be an important topic. Chemical warehouse incidents related to fire and explosi...

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AbstractThere were many wake‐up calls in past decades right from the early industrialized Europe to the present century, from the Americas and to Asia. In Asia, the most prominent process safety incident was the 1984 Bhopal accident in India that greatly impacted the chemical industry. Today, it is the prerequisite for the management of major hazards installations (MHIs) such as petroleum refining, petrochemical manufacturing facilities, and chemical processing plants to be fully aware of their responsibility to ensure meeting safety, health, and environment requirements. Any MHI failure would not only lead to operation slowdown that result in financial loss, but also to worst‐case scenarios. One typical example is hazardous material release to the atmosphere as occurred in Bhopal which can result in catastrophic consequence.We must always maintain a sense of vulnerability, learning from past incidents, adherence to code of practices, and standards for new as well as existing equipment. Such a proactive approach will definitely reduce the risk of MHI to as low as reasonably applicable.

Concealed risk in catalytic processes: How weather can initiate a catastrophe in an ethylbenzene-producing tower

In a Taiwanese chemical processing plant producing ethylbenzene, an unexpected thermal runaway reaction occurred in an alkylation tower during shutdown, causing approximately 7500 kg of catalyst to be wasted. Prior risk assessments failed to identify the responsible hazard vectors. The thermal runaway occurred despite all former appropriate operating procedures being strictly followed. To identify the root cause of this incident, the generation and dissemination of the thermal source were tested and simulated using thermal analysis methods and a prototype tower. The results showed that water adsorption could provide heat to the catalyst bed, even at temperatures exceeding 350 °C, which is the temperature required for the oxidation of hydrocarbon adsorbates. High humidity caused by Typhoon Lionrock was a critical factor contributing to the incident, which was not anticipated by the plant engineers. Humidity within the air inlet supplied the abundant adsorption heat required to increase the bed temperature. This resulted in thermal runaway due to heavy oxidation of the large number of adsorbates. This study proposes a safer strategy that involves passing humid N2 through the catalyst tower to prevent the oxidation of hydrocarbon adsorbates, thereby removing the adsorption heat. The purpose of this study was to reveal hazards concealed in the catalyst system. If a catalyst has a noticeable adsorption heat that, at a given temperature, can induce spontaneous oxidation or decomposition of the adsorbates with a possible thermal runaway reaction. This paper interprets past failures as lessons and proposes solutions that can be used by those in the chemical manufacturing industry to avert such incidents in the future.

Simulation of Stratified Two-phase Flow Regime using Air-Water Model in ANSYS Fluent®

Two-phase flow has widely grown in importance in various fields of science and engineering system such as nuclear reactors, heat exchangers, transport system, chemical processing plants etc. One of the major issues faced by these sectors is the development of an accurate and reliable multiphase flow measurement system. At present there is no single system which could measure the two phase flow parameters irrespective of its incoming conditions. Characterization of the single phase flow parameter is easier when compared with two phase flow as the velocity distribution, mass flow rate and void fraction of each component phases are not easily measurable. The other factors that add to these difficulties are the prediction of flow regimes and the effect of pipe or channel orientation on the flow properties in the two phase flow. The hold-up of the individual phases and their relative velocity along with pressure drop needs to be taken care while dealing with these flows. Various researches have clearly shown that no correlation can be used for predicting the flow regimes in a two phase flow satisfactorily. By using volume of fluid multiphase flow model air water two phase flow was simulated for inlet velocities of water and air as 0.121 m/s and 6.56 m/s respectively. Stratified laminar flow was obtained from the CFD simulation. Variation in volume fraction of both air and water, mass flow rate and pressure drop with respect to change in time are investigated using the simulation.

Analysis of a full-scale slope failure test on a sludge embankment

In the framework of the reorganisation of several historical wet deposits of a chemical processing plant, one of the oldest wet deposits was cleared because of environmental and economic considerations. The stored sludge (around 350 000 m3) was transported to the neighbouring environmentally controlled deposit, Veldhoven (Ham, Belgium), and stacked on top of existing sludge, using the sludge itself as dyke construction material. To calibrate the embankment stability analysis procedure, a full-scale slope failure test was conducted on an embankment built with industrial calcium fluoride (CaF2) sludge. The Norwegian Geotechnical Institute–active, direct simple shear, passive (NGI–ADP) material model was used to perform a finite-element back-analysis of the full-scale slope failure test. Anisotropic conversion factors are suggested to convert the in situ measured vane shear strengths to shear strengths at different anisotropic stress states, which can then be implemented in the NGI–ADP model. The simple and quickly executable in situ vane shear test was used to master the uncertainties linked to the heterogeneity of the sludge. Sludge heterogeneity not only depends on sludge composition, but also on manipulations during transport and handling of the sludge at the construction of the elevations. The results of the full-scale slope failure test support the proposed modelling of the embankment stability problem.

Identification and Diagnosis of Fault in Consistent Mixed Tank Reactor using Neural System

The Consistent Mixed Tank Reactor is widely used in chemical processing plants, food processing industries and in pharmaceutical industries. The Consistent Mixed Tank Reactor is a nonlinear process. If any of inputs to the process or any parameters in the process gets changed means the entire working of the plant gets changed. And also it leads to decrease the accuracy of the final or end product. So, to get required end result the accuracy developed in the Consistent Mixed Tank Reactor process where the tank is in running condition need to be identified and necessary steps should be taken to avoid those fault. There are several fault diagnosis methods available. Among those techniques, the Neural System Prescient Controller can be utilized to identify and diagnose the error present inside Consistent Mixed Tank Reactor tank. From the Neural System Prescient Controller, the servo response is obtained to understand clearly about the behavior of the Consistent Mixed Tank Reactor tank process. Thus, by selecting suitable controlling techniques the accuracy of the desired product can be obtained in Consistent Mixed Tank Reactor tank.

Utility Paths Combination in HEN for Energy Saving and CO2 Emission Reduction

Energy demand and flue gas emissions, namely carbon dioxide (CO2) associated with the industrial revolution have exhibited a continuous rise. Several approaches were introduced recently to mitigate energy consumption and CO2 emissions by either grass root design or retrofit of existing heat exchanger networks (HEN) in chemical process plants. In this work, a combinatorial approach of path combination is used to generate several options for heat recovery enhancement in HEN. The options are applied to successively shift heat load from HEN utilities using combined utility paths at different heat recovery approach temperature (HRAT) considering exchangers pressure drop. Industrial case study for HEN of the preheat train in crude oil distillation unit from the literature is used to demonstrate the approach. The obtained results have been studied economically using the cost targeting of Pinch Technology. As a result, both external energy usage and CO2 emissions have been reduced from a heater device in HEN by 20% and 17%, respectively, with a payback of less than one year.

Development of surrogate model using CFD and deep neural networks to optimize gas detector layout

To reduce damage arising from accidents in chemical processing plants, detection of the incident must be rapid to mitigate the danger. In the case of the gas leaks, detectors are critical. To improve efficiency, leak detectors must be installed at locations after considering various factors like the characteristics of the workspace, processes involved, and potential consequences of the accidents. Thus, the consequences of potential accidents must be simulated. Among various approaches, computational fluid dynamics (CFD) is the most powerful tool to determine the consequences of gas leaks in industrial plants. However, the computational cost of CFD is large, making it prohibitively difficult and expensive to simulate many scenarios. Thus, a deep-neural-network-based surrogate model has been designed to mimic FLACS (FLame ACceleration Simulator), one of the most important programs in the modeling of gas leaks. Using the simulated results of a proposed surrogate model, a sensor allocation optimization problem was solved using mixed integer linear programming (MILP). The optimal solutions produced by the proposed surrogate model and FLACS were compared to verify the efficacy of the proposed surrogate model.

Comparing the Economics of Metal Additive Manufacturing Processes for Micro-scale Plate Reactors in the Chemical Process Industry

Abstract Modular chemical process intensification involves the deployment of process intensification technology within the modular construction of chemical processing plants. Process intensification involves improvements in chemical process plant efficiency that drive size and weight reductions in chemical equipment. The use of smaller, lighter weight equipment reduces costs in modular construction leading to lower capital thresholds for pursuing new distributed and specialty chemical markets. Micro-scale plate reactors, with high surface-area-to-volume ratios, provide a platform for reducing the size and cost of chemical reactors due to high rates of heat and mass transfer. However, widespread adoption of this technology is hampered by economics and the lack of existing supply chains. Additive manufacturing processes have the potential to overcome this roadblock. This paper compares the economics of using two prominent metal additive manufacturing processes to produce micro-scale plate reactors.

HML-IDS: A Hybrid-Multilevel Anomaly Prediction Approach for Intrusion Detection in SCADA Systems

Critical infrastructures, e.g., electricity generation and dispersal networks, chemical processing plants, and gas distribution, are governed and monitored by supervisory control and data acquisition systems (SCADA). Detecting intrusion is a prevalent area of study for numerous years, and several intrusion detection systems have been suggested in the literature for cyber-physical systems and industrial control system (ICS). In recent years, the viruses seismic net, duqu, and flame against ICS attacks have caused tremendous damage to nuclear facilities and critical infrastructure in some countries. These intensified attacks have sounded the alarm for the security of the ICS in many countries. The challenge in constructing an intrusion detection framework is to deal with unbalanced intrusion datasets, i.e. when one class is signified by a lesser amount of instances (minority class). To this end, we outline an approach to deal with this issue and propose an anomaly detection method for the ICS. Our proposed approach uses a hybrid model that takes advantage of the anticipated and consistent nature of communication patterns that occur among ground devices in ICS setups. First, we applied some preprocessing techniques to standardize and scale the data. Second, the dimensionality reduction algorithms are applied to improve the process of anomaly detection. Third, we employed an edited nearest-neighbor rule algorithm to balance the dataset. Fourth, by using the Bloom filter, a signature database is created by noting the system for a specific period lacking the occurrence of abnormalities. Finally, to detect new attacks, we combined our package contents-level detection with another instance-based learner to make a hybrid method for anomaly detection. The experimental results with a real large-scale dataset generated from a gas pipeline SCADA system show that the proposed approach HML-IDS outperforms the benchmark models with an accuracy rate of 97%.

Integrating A Dynamic Simulator and Advanced Process Control using the OPC-UA Standard

Abstract Insufficient interoperability has long been an issue on the factory floor, however, new technologies and standards are enabling production systems to become more agile and interoperable. A communication standard can, for example, make interoperation among different vendor-specific software and hardware tools in production systems easier and more reliable. In this paper, we share our research results and experience for the establishment of a connection between a dynamic simulator and an advanced process controller in a manufacturing system using OPC-UA. The OPC-UA communication protocol, which is middleware, acts as a common interface between these systems. We established the client and server for communication and defined an exchange data structure based on the OPC-UA standard for a control problem in a chemical process plant. The case study is a proof of concept of the OPC-UA standard implementation to support interoperability for different domains.

Solving Fresnel equation for refractive index using reflected optical power obtained from Bessel beam interferometry.

This work demonstrates an interferometric technique to estimate the reflected powers from dielectric interfaces and the reflection coefficient using the Fresnel equation for measurement of the refractive index (RI) of liquid samples. It uses low-coherence common-path optical interferometry that is commonly used for optical imaging. A uniquely designed optical fiber tip generating a high-quality non-diffractive Bessel beam probes liquid samples in a glass container non-invasively. The light reflected from different interfaces of the container is recollected by the same optical fiber tip. The reflected beams interfere with the reference beam generated at the fiber tip itself. This interference spectrum is further processed using fast-Fourier transform to measure reflected powers from the respective interfaces. The acquired powers are used to solve the Fresnel equation to find RI of liquid samples. As a proof of concept, experiments have been performed on several liquid samples including turbid media such as blood. This non-invasive interferometric technique could also be an ideal example confirming the Fresnel equation for reflection of light. Unlike other optical fiber-based RI sensors, this technique does not require temperature compensation. The method can be employed for inspection of the production process in terms of RI in pharmaceutical and chemical process plants, etc.

Emissions Reduction of a Chemical Process Plant by Maximizing Energy Recovery, Minimizing Energy Losses and Fuel Switching

Emissions Reduction of a Chemical Process Plant by Maximizing Energy Recovery, Minimizing Energy Losses and Fuel Switching

Problems and Perspectives in Seismic Quantitative Risk Analysis of Chemical Process Plants

Earthquakes represent a class of natural-technical (NaTech) hazards which in the past have been responsible of major accidents and significant losses in many industrial sites. However, while codes and standards are issued to design specific structures and equipment in both the civil and industrial domain, established procedures for seismic quantitative risk assessment (QRA) of process plants are not yet available. In this paper, a critical review of seismic QRA methods applicable to process plants is carried out. Their limitations are highlighted and areas where further research is needed are identified. This will allow to refine modeling tools in order to increase the capabilities of risk analysis in process plants subjected to earthquakes.

The set covering problem applied to optimisation of gas detectors in chemical process plants

An approach to optimise the number and location of gas detectors was developed based on the colour pattern of the graph of the set covering problem (SCP). The optimisation problem is combined to Computational Fluid Dynamics (CFD) data and the discrete optimisation problem is solved using Balas algorithm in the framework of a developed Fortran code. Every computational cell is regarded as a node of a graph where the links are the common boards shared by the neighbouring cells. The graph is read into the code via a dedicated algorithm for generation of the set of constrains. The characteristic length that determines the distance among the nodes of the graph is obtained from the set of CFD simulations or any stablished criterion. This work is based on the discovery of the colour pattern observed in the adjacency matrix that represents the set of constrains of the optimisation formulation and also on the limited set of gas dispersion CFD simulations to ensure full coverage of the area. Two cases (covering problem and p-median problem) are used in the validation of the developed code. Results are compared with the results obtained using CPLEX. Additional tests are considered ranging from simple 2D cases with 4 nodes up to 75 nodes. Two real engineering cases are considered and the efficiency of the method is discussed based on the volume of the cloud detected and the time to detection. Results show an efficient method for optimisation of gas detectors able to detect flammable clouds as small as a few cubic meters within 10 seconds.

A new fuzzy methodology-based structured framework for RAM and risk analysis

The aim of this paper is to propose a new hybridized framework for analyzing the performance issues of a chemical process plant by utilizing uncertain, imprecise and vague information. In the proposed framework, Fuzzy Lambda–Tau (FLT) approach has been used for computing reliability, availability and maintainability (RAM) parameters of the considered system. Further, for enhancing the RAM characteristics of the system, improved Fuzzy Failure Mode Effect Analysis (FMEA) approach is adopted. Under improved Fuzzy FMEA approach, defined Fuzzy linguistic rating values in the form of triangular and trapezoidal Fuzzy numbers have been assigned by the experts to each risk factor of the listed failure causes. The proposed framework is demonstrated with an industrial application in a chlorine production plant of a chemical process industry. The results show decreasing trend for system availability and deposition of solid Nacl, mechanical failure, corrosion due to wet chlorine, scanty lubrication, improper adsorption and valve malfunctioning are identified as the most critical failure causes for the considered system. A comparative performance analysis between the proposed framework, Fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS), Fuzzy evaluation based on distance from average solution (Fuzzy EDAS) and Fuzzy Vlse Kriterijumska Optimizacija I Kompromisno Resenje (Fuzzy VIKOR) are then carried out to show the competence of the proposed framework. It is expected that the analytical results would be highly useful in formulating an optimal maintenance policy for such complex systems and may also be used for improving performance of similar plants.

Carbon-14 content in tree and soil samples at the Idaho National Laboratory nuclear site

Idaho National Laboratory (INL) is a nuclear research facility located in southeastern Idaho, USA; over the course of its operational history, INL has operated 52 reactors and 1 reactor fuel reprocessing facility. To determine the extent to which previous nuclear operations at INL have impacted local environmental carbon-14 (14C) concentrations, tree and soil samples from the INL desert and surrounding areas were collected, combusted, and analyzed by Accelerator Mass Spectrometry (AMS). Transport models of the plumes from the Advanced Test Reactor Facility (ATR) and the Idaho Chemical Processing Plant (ICPP) suggest the historic annual integrated plume distributions from each of these source terms was most likely in the northeast-southwest direction, with very little ground contact from stack emissions in the immediate vicinity of the facilities and maximum estimated ground contact arising ∼400–1000 m to the northeast/southwest from each facility. 14C data from annual growth ring data from trees located immediately adjacent to the ATR, INL’s Central Facilities Area (CFA), and Mud Lake, Idaho (ML) are in statistical agreement with regional nuclear weapons testing fallout backgrounds. Surface soil samples taken near a low level radioactive disposal facility and downwind from the ICPP show percent modern carbon (pMC) values ranging from 28 ± 2 to 92 ± 4, suggesting a mixture of aged and modern background carbon containing materials. Taken together, these data suggest that 14C in the INL region is predominantly derived from a mixture of aged and modern background sources (e.g. nuclear weapons testing fallout), with insignificant contributions from INL source terms.

High temperature mechanical deformation of an additive manufactured nickel based superalloy using small scale test methods

Nickel based superalloys have been utilised within numerous industrial sectors from power generation to chemical processing plants for over four decades as a result of their ability to retain mechanical properties at arduous temperatures alongside excellent oxidation and corrosion resistance. Within the aerospace industry, they have been primarily used within regions of the gas turbine engine where metal temperatures can often exceed 1000°C and high temperature deformation mechanics are prominent. Although typically manufactured using traditional wrought and casting methodologies, the aerospace industry has become increasingly interested in the use of Additive Layer Manufacturing (ALM) as a means of fabrication to take advantage of the numerous benefits that ALM has to offer. Detailed characterisation of the structural integrity of components processed via additive processes is a key requirement of the understanding. In this paper, the small punch creep (SPC) test has been applied to samples of a high gamma prime containing nickel-based superalloy manufactured using the laser powder bed fusion (LPBF) process. Several different builds are investigated and ranked, with ALM builds provided in different epitaxial orientations and with contrasting process parameters to help determine the optimal process parameters.

Development and Application of Fire & Explosion Risk Index Methods to Chemical Process Plants

Fire and explosion risk evaluation in the industrial context is a fundamental tool for work owners and safety manager to individuate critical scenario and issues related to fire and explosion in industrial facilities and sites. The primary objective of the risk evaluation is the definition of possible accident scenarios, their likelihood and consequences concerning damages to people and facilities, as to define an adequate fire strategy and preventive-protection measures. In this work, three real accidents were tested with three real fire and explosion risk evaluation methods, among the most adopted worldwide: FE The Mond Index, by Imperial Chemical Industries; Safety Weighted Hazard Index (SW&HI), by Khan, Husain, Abbasi (2001).All these methodologies could be classified among semi-quantitative index methods, and their outputs are quantitative values (indexes) which indicate to analysts the most hazardous units or processes and help to define priorities on protection system implementation. A new index, incorporating the incomes of the three methods adopted is proposed in this paper. A sensitivity analysis was also performed, as to investigate whether operating conditions or peculiar characteristics of substances involved in processes has the more significant influence on the risk evaluation, according to the method adopted. This work has also posed the basis to develop a novel method, derived mainly from the SW&HI methodology: this will apply to a wide range of industrial facilities and incorporate relevant features of previous methodologies attempting to eliminate critical points and issues of former methods.

The Analysis of Chemical Engineering Process Plants and their Models Represented by Networks

Chemical process plants can be interpreted as networks where energy mass and momentum are exchanged. Therefore, they can be represented as such using graph theory. This work presents a decomposition methodology that allows to conceptually deconstruct chemical process plants and their models to be represented by networks. By the selection of minimum constituent units of the plant, it is possible to describe the interdependencies among those minimum units. This procedure allows the correct representation of process plants as networks along with a detailed analysis of them. Using network metrics such as centrality measures, it was possible to analyze, as an example, the HDA process plant under two selected plantwide control strategies, allowing the identification of its more important units and controllers. This permitted an insight of how plantwide control strategies responded to perturbations from a qualitative point of view. It was also possible to establish the main coincidences and discrepancies between the selected plantwide control strategies.