Chemical Process Industries Research Articles

Multiscale monitoring of industrial chemical process using wavelet-entropy aided machine learning approach

In recent decades, machine learning (ML) techniques have been effectively applied for industrial process monitoring to assure safety and high-quality yield. Traditional process fault detection, identification, and diagnosis (FDI&D) approaches are insufficiently smart to address the modern complex challenges of real-time industrial chemical processes. The detection and diagnostic resolution of the traditional monitoring approach are less robust, inefficient, and produce the wrong interpretation of actual fault information. These approaches are based on a single-scale fault illustration and cannot effectively address multiple fault depiction roots. This study introduces a novel ML-aided methodological framework for industrial and manufacturing monitoring systems. The proposed ML framework is developed using Principal component analysis (PCA), Shannon information entropy (IE), wavelet transformation (WT), and signed directed graph (SDG). It includes fault detection, identification, and diagnostic propagation root-path interpretation to address the safety challenges of modern, real-time industrial chemical processes. The proposed methodological framework is validated using the Tennessee Eastman process (TEP) benchmark to highlight their performance and efficiency. The results of this study determined that the new proposed approach is more efficient in terms of accuracy, robustness, and actual propagation root cause than traditional techniques. It has a high fault detection rate (FDR), low fault alarm rate (FAR) that identifies and recognizes the actual faulty-correlated variables to establish the SDG model framework for determining the actual diagnostic propagation root path. It initially enables operators to react to unusual incidents, ensuring industrial safety, minimizing economic loss, and avoiding disasters.

A functional data-driven approach to monitor and analyze equipment degradation in multiproduct batch processes

Equipment degradation is ubiquitous in the Chemical Process Industry (CPI), causing significant losses in efficiency, controllability, and plant economy, as well as an increased environmental fingerprint and additional operational safety risks. The case of fouling in heat exchangers, in particular, is well-known and pervasive but still hard to cope with, given the complexity of the underlying mechanisms and the difficulty of assessing its extension in real-time. This problem becomes even more complex in batch processes producing different products, where multiple recipes are used, bringing additional variability and new challenges to the analysis. In this work, we propose a functional data-driven approach for streamlining the analysis and monitoring of the progression of fouling taking place in heat exchangers in multiproduct batch processes. With the approach developed and presented in this paper, process analysis can be efficiently conducted by integrating historical data with engineering knowledge. Furthermore, a surrogate measure of fouling extension in heat exchangers is proposed, that can be readily implemented as an equipment health indicator (EHI) leading to a safer operation of the heat exchanger.

A brief review on systematic approach to polymer selection for development of capillary/hollow-fibre membrane for practical applications

Polymeric membranes are widely used for treatment of lean stream in chemical process industries. These membranes are used in different configurations such as tubular, plate & frame, disc-tube, spiral and capillary/hollow-fibre. Membrane modules with capillary/hollow-fibre configuration appears promising in ultrafiltration applications due to its relatively higher packing density, ease of backwashing, ease of cleaning and lower pressure drops since it can be operated at laminar flow regime with high cross-flow velocity. Hence, efforts are being made by researchers to make capillary/hollow-fibre membrane modules from various polymers, ranging from most hydrophilic polyacrylonitrile (PAN) to super-hydrophobic polypropylene (PP) and polytetrafluorethylene (PTFE). Here, we discuss the qualifying properties of the polymeric materials suitable to spin into capillary/hollow-fibre ultrafiltration membranes. Selection of polymers for making fibres requires in-depth knowledge of properties of base polymer and its processability/fabricability. The important properties to be considered for making capillary/hollow-fibre membrane are intrinsic structural properties of the base polymer like degree of crystallinity, tensile strength, tensile modulus, etc. The functional properties such as permeability, hydrophilicity/hydrophobicity etc. also plays role in selecting polymer for a given application. The polymer should also have appreciable dissolution in available solvents or should have degradation temperature higher than melting point so that it can be processed through appropriate membrane preparation process.

Unleashing the potential of scalable, high-water vapor permeance graphene oxide membranes using electrospun supports

Dehumidification and dehydration technologies are projected to consume a substantial portion of the world's energy demand through the HVAC, and chemical processing industries. The development of highly permeable water-selective membranes represents a potential path to revolutionize these industries by drastically reducing their energy requirements. Selective layers based on graphene oxide (GO) have demonstrated the highest water vapor permeances to date, making GO a promising candidate for reducing the footprint and costs associated with commercial module development. However, most studies primarily focus on small-area membranes fabricated utilizing vacuum or pressure filtration, leading to either thick free-standing films or thin layers supported on model substrates such as anodic aluminium oxide. In this work, a casting approach is deployed to coat thin GO layers onto large-area membrane supports. In addition to the application of scalable methods, commonly used supports act as a significant bottleneck to the overall permeance. By designing highly porous (up to 92%) electrospun polystyrene supports, we demonstrate a 27% increase in overall permeance as compared to commercial Nylon and polyethersulfone supports even though these membranes have thicker GO selective layers. Despite the pore size in the electrospun material being larger than the GO flakes, the GO forms a selective layer via casting, and achieved water to nitrogen selectivity larger than 103.

Real-time three-dimensional micro-imaging of liquid front in spreading sessile droplet under transient spreading states.

Dynamic wetting behaviors of sessile droplets on substrates play crucial roles for various industrial chemical processes. In the case of complete wetting, it has been proposed that a precursor film which is nanometer-order thickness and micrometer-order length further expands outside the macroscopic contact line of the sessile droplet. While the time evolution of the precursor film is believed to strikingly affect the macroscopic wetting behavior (e.g., spreading velocity), it had been hard to visualize the three-dimensional shape of the precursor film at the early stage of wetting. Further, although the spreading velocity rapidly decreases upon time at the early stage of the wetting (transient state) and then converged to be a constant at later time (steady state), conventional fluid mechanics theories generally describe only the wetting behavior at the steady state. Therefore, experimental observation of time evolution of the precursor film shape in the transient state is essential to proceed the theories to the next step. Here, a monochromatic laser interference microscope was developed to visualize three-dimensional shape of the wetting front of sessile droplets in real time. By detecting an interference of the laser reflected at the liquid and substrate surfaces, the precursor film was successfully visualized with a time resolution of 20ms and a thickness resolution of about 3.5nm at worst. For 10 cSt silicone oil on Si substrate, a 60-nm-thick and 70-μm-long precursor film was observed for 2-10min after dropping and its spreading velocity which decreased with time was quantitatively analyzed.

Particle coating growth behaviors in a spray fluidized bed based on Gas-Liquid-Solid Quasi-Three-phase DEM numerical simulation

Wet granulation processes are widely used in chemical, food, pharmaceutical, and various other industrial processes. During the process of particle coating growth, particle fluidization state and liquid characteristics have an impact on the layer's thickness and homogeneity. In this study, discrete element, liquid bridge, and droplet motion models were combined to investigate the impact of injection gas velocity, droplet diameter and injection liquid viscosity on the particle growth ratio and coating thickness distribution, and the particle growth mechanism was investigated. The particle growth area, which is the area where the number of coated particles exceeds 0, was significantly expanded by decreasing the droplet diameter and increasing the gas velocity at appropriate ranges. The frequency of droplet and particle collisions increased, and average relative liquid content decreased with increasing particle growth area. By accelerating the injection of gas and reducing the viscosity of the injected liquid, the fluidization condition of the particles, which primarily affected the particle cycle frequency, could be enhanced. The improvement of particle growth area and particle cycle frequency allowed more particles to participate in growth and improved the quality of the grown particles. The proportion of small particles dramatically grew while the proportion of large particles reduced. The standard deviation of particle diameter decreased, and particle growth became more uniform.

Electrification of steam cracking as a pathway to reduce the impact of the petrochemical industry on climate change

In the Chemical Process Industry (CPI) only hydrogen production from steam methane reforming produces more greenhouse gas emissions than light olefins production. Various solutions have been proposed to reduce the CO2 emissions from olefins production. It is not clear which solution is best, particularly when you consider other sustainability factors. In this paper we report the results of our cradle-to-gate life cycle assessment (LCA) of three highly promising solutions: a so-called low-emission steam cracking furnace, the electrically driven RotoDynamic reactor (RDR), and a blue hydrogen-fired furnace. Life cycle inventory data is obtained from a first principles model for steam cracking, validated with industrial data, and combined with data obtained from process simulation. Our study shows that if the electrical supply is grey then the RDR-based cracking process has a 41% higher impact on climate change than the reference base case, a conventional plant with state-of-the-art furnaces. The low-emission furnace is only 7.2% higher. When using a mixed electrical grid, like Belgium's, the climate change impact for the RDR is 1.5% higher and the low emission furnace is 6.6% lower. With a grid that uses fully renewable power generation the RDR solution is an impressive 27% lower than the base case and the low-emission furnace 17% lower. We also analysed the impact of firing the furnaces with blue hydrogen from (a) a conventional steam-methane reformer and (b) an innovative gas-heated reformer: both (a) and (b) using carbon capture and storage (CCS). The climate change impact is reduced by 8% and 18% respectively compared to the base case. Since the blue hydrogen solution uses very little electricity, the climate change impact is insensitive to the method of electrical power generation.

Materials for Hydrogen Storage and Transport: Implications for Risk-Based Inspection

The growing interest towards hydrogen technologies and their implementation in the hydrocarbon and chemical process industry makes maintenance planning of storage and transport equipment an emerging safety aspect. With respect to high-pressure working equipment, Risk-Based Inspection methodology (RBI) aims at minimizing the risk of loss of containment due to materials’ deterioration mechanisms. This set of procedures focuses on the mechanical integrity of equipment to achieve crucial risk mitigation by means of risk-informed inspection planning and maintenance activities. In addition, hydrogen-induced damages are often generalized or even neglected by the existing RBI standards and recommended practices. On this basis, high-pressure vessels, process piping and storage tanks working in gaseous or liquid hydrogen environments, which are exposed to hydrogen-induced deterioration mechanisms, might be subjected to an inaccurate evaluation of the associated risk and hazards when these RBI standards are applied. For this reason, this work proposes a review of the pipelines steels commonly used for gaseous hydrogen transport to investigate the possible limitations of the standard RBI planning methodologies, when applied to hydrogen technologies. More accurately, the pipeline steels’ susceptibility to hydrogen-induced degradations mechanisms will be discussed to underline assumptions and hypothesis limiting the conventional RBI applicability. Therefore, the overall suitability of standard RBI planning with respect to hydrogen equipment is discussed, highlighting possible relevant gaps as a general result.

Computational insights into deep eutectic solvent design: Modeling interactions and thermodynamic feasibility using choline chloride & glycerol

To address climate change and environmental concerns, the chemical process industries are increasingly adopting green technology and exploring eco-friendly waste management practices. Deep Eutectic Solvents (DES) have emerged as promising green solvents with diverse applications in the industries in separation processes, catalysis, drug design, and a lot more. However, the ideal way for modeling ionic interactions within the hydrogen-bond acceptor (HBA) during DES synthesis remains uncertain, and theoretical insight on the formation mechanism of energetically stable DESs from choline chloride (CHL) and urea (URE) is yet to be explored. In this study, we investigate different modeling approaches (no bond, generic bond, or single bond) for ionic interactions in hydrogen-bond acceptor (HBA) molecules during DES synthesis and the thermodynamic feasibility of forming an energetically stable DES using choline chloride (CHL) and urea (URE) through theoretical calculations (PM3, HF, and DFT). Our findings indicate that the choice of modeling approach does not significantly affect the outcomes across calculation levels. The H-X DES formation pathway emerges as the most viable route for CHL: GLC DES synthesis, offering strong binding energy and high exothermicity. This pathway provides a robust solution for synthesizing energetically stable DES with potential applications in various industries. Researchers can consider alternative calculation levels when DFT becomes cost-prohibitive to optimize computational costs. Our study contributes valuable insights into DES design and synthesis, promoting environmentally sustainable and cost-effective processes.

Coking-Produced Aromatic Compounds in Urine of Exposed and Nonexposed Populations: Exposure Levels, Source Identification, and Model-Based Health Implications.

Coking contamination in China is complex and poses potential health risks to humans. In this study, we collected urine samples from coking plant workers, nearby residents, and control individuals to analyze 25 coking-produced aromatic compounds (ACs), including metabolites of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, chlorophenols, and nitrophenols. The median concentration of total ACs in urine of workers was 102 μg·g-1 creatinine, significantly higher than that in the other two groups. Hydroxy-PAHs and hydroxy hetero-PAHs were the dominant ACs. Workers directly exposed from coking industrial processes, i.e., coking, coal preparation, and chemical production processes, showed higher concentrations of hydroxy-PAHs and hydroxy hetero-PAHs (excluding 5-hydroxyisoquinoline), while those from indirect exposure workshops had higher levels of other ACs, indicating different sources in the coking plant. The AC mixture in workers demonstrated positive effects on DNA damage and lipid peroxidation with 5-hydroxyisoquinoline and 3-hydroxycarbazole playing a significant role using a quantile g-computation model. Monte Carlo simulation revealed that coking contamination elevated the carcinogenic risk for exposed workers by 5-fold compared to controls with pyrene, pentachlorophenol, and carbazole contributing the most, and workers from coking process are at the highest risk. This study enhances understanding of coking-produced AC levels and provides valuable insights into coking contamination control.

An industrial case study on the combined identification and offset-free control of a chemical process

For three decades, model predictive control (MPC) has been the flagship advanced control method in the chemical process industries. However, most implementations still use heuristic methods for designing MPC estimators, especially for offset-free MPC implementations. In this paper, we present a recently developed maximum likelihood-based method for the identification of linear augmented disturbance models for use in offset-free MPC. This method provides noise covariances that are used to derive Kalman filters and moving horizon estimators, forgoing the need for manual design and tuning of the estimator. The method is extended to handle closed-loop plant data. The proposed identification method and estimator design are evaluated in industrial-scale, real-world case study of a process at Eastman Chemical’s Kingsport plant. Using this identified model, we reduced the mean stage cost by 38% compared to the performance of the existing, hand-tuned MPC model.

Polymer-Based Thermally Stable Chemiresistive Sensor for Real-Time Monitoring of NO2 Gas Emission.

We present a thermally stable, mechanically compliant, and sensitive polymer-based NO2 gas sensor design. Interconnected nanoscale morphology driven from spinodal decomposition between conjugated polymers tethered with polar side chains and thermally stable matrix polymers offers judicious design of NO2-sensitive and thermally tolerant thin films. The resulting chemiresitive sensors exhibit stable NO2 sensing even at 170 °C over 6 h. Controlling the density of polar side chains along conjugated polymer backbone enables optimal design for coupling high NO2 sensitivity, selectivity, and thermal stability of polymer sensors. Lastly, thermally stable films are used to implement chemiresistive sensors onto flexible and heat-resistant substrates and demonstrate a reliable gas sensing response even after 500 bending cycles at 170 °C. Such unprecedented sensor performance as well as environmental stability are promising for real-time monitoring of gas emission from vehicles and industrial chemical processes.

Spatial weighted graph-driven fault diagnosis of complex process industry considering technological process flow

Each chemical process industry system possesses unique process knowledge, which serves as a representation of the system’s state. As graph-theory based methods are capable of embedding process knowledge, they have become increasingly crucial in the field of process industry diagnosis. The fault representation ability of the diagnosis model is directly associated with the quality of the graph. Unfortunately, simple fully connected graphs fail to strengthen the internal connections within the same process but weaken the interactive connections between different processes. Moreover, each node in the graph is considered equally important, making it impossible to prioritize crucial system monitoring indicators. To address the above shortcomings, this paper presents a spatial weighted graph (SWG)-driven fault diagnosis method of complex process industry considering technological process flow. Initially, the physical space sensor layout of the technological process flow is mapped into the spatial graph structure, where each sensor is regarded as a node and these nodes are connected by the k nearest neighbor algorithm. Subsequently, according to the mechanism knowledge, the sensors in the process are divided into different importance categories and weight coefficients are assigned to their nodes. The similarities between these weighted nodes are calculated, and the resulting edge information are used to construct the SWGs. Finally, the SWGs are input to a graph convolutional network, facilitating fault representation learning for fault diagnosis of complex process industry. Validation experiments are conducted using public industrial datasets, and the results demonstrate that the proposed method can effectively integrate the process knowledge to improve the fault diagnosis accuracy of the model.

Transforming the Petroleum Industry through Catalytic Oxidation Reactions vis-à-vis Preceramic Polymer Catalyst Supports.

Preceramic polymers, for instance, are used in a variety of chemical processing industries and applications. In this contribution, we report on the catalytic oxidation reactions generated using preceramic polymer catalyst supports. Also, we report the full knowledge of the use of the remarkable catalytic oxidation, and the excellent structures of these preceramic polymer catalyst supports are revealed. This finding, on the other hand, focuses on the functionality and efficacy of future applications of catalytic oxidation of preceramic polymer nanocrystals for energy and environmental treatment. The aim is to design future implementations that can address potential environmental impacts associated with fuel production, particularly in downstream petroleum industry processes. As a result, these materials are being considered as viable candidates for environmentally friendly applications such as refined fuel production and related environmental treatment.

SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes

Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts of data can be difficult in industrial settings.In this paper, we propose SensorSCAN, a novel method for unsupervised fault detection and diagnosis, designed for industrial chemical process monitoring. We demonstrate our model's performance on two publicly available datasets of the Tennessee Eastman Process with various faults. The results show that our method significantly outperforms existing approaches (+0.2-0.3 TPR for a fixed FPR) and effectively detects most of the process faults without expert annotation. Moreover, we show that the model fine-tuned on a small fraction of labeled data nearly reaches the performance of a SOTA model trained on the full dataset. We also demonstrate that our method is suitable for real-world applications where the number of faults is not known in advance. The code is available at https://github.com/AIRI-Institute/sensorscan.

A Review of the Mechanical Properties of 17-4PH Stainless Steel Produced by Bound Powder Extrusion

17-4PH Stainless Steel is a mechanically high-performing alloy that is widely used across chemical and mechanical processing industries. The alloy is conventionally fabricated by cast methods, but emerging additive manufacturing techniques are presently offering an economic, efficient, and environmentally friendly alternative. Bound Powder Extrusion (BPE) is a relatively new additive manufacturing technique that is used to fabricate three-dimensional, free-form components. Investigation into the mechanical properties and behavior of 17-4PH stainless steel fabricated by BPE is vital to understanding whether this technique proposes a competitive substitute to the cast alloy within industry. Published literature has investigated the as-fabricated mechanical properties, microstructure, porosity, and post-processing heat treatment of the BPE alloy, with limited comparison evident among the papers. This paper, therefore, aims to review published findings on the mechanical properties of 17-4PH stainless steel produced by additive manufacturing techniques, with a key focus on BPE. It is important to highlight that this review study focuses on the MetalXTM 3D printer, manufactured by Markforged. This printer is among the widely utilized BPE 3D printers available in the market. The key results, together with the impact of post-heat treatments, were discussed and compared to provide a more comprehensive picture of the patterns that this alloy presents in terms of its microstructure and mechanical properties. This enables the manufacture of components relative to desired material performance, improving overall functionality. A comparison of yield strength, ultimate tensile strength (UTS), Young’s modulus, ductility, and hardness was made relative to microstructure, porosity, and density of published literature for the as-fabricated and post-heat-treated states, identifying areas for further research.

Designing, evaluating, and benchmarking auditory alarms for the chemical and oil processing industry

The auditory alarms used in oil and chemical processing control rooms are often based on practices and knowledge which is twenty to thirty years out of date, and therefore do not embody the significant progress that has been more recently made in this area. Best practice available from other areas (aviation, transport, and healthcare) can be brought to bear in improving and updating control room alarms as they share similar problems with alarms in those domains. This paper describes the processes of designing, benchmarking, and testing a series of sets of auditory alarms intended for use in control rooms by the oil and chemical processing industry. In particular, the work shows how important the localizability of alarms can be in practice, and how improved localizability can be designed into auditory alarms.

VDM 33

Abstract VDM 33 (Nicrofer 3033) is an austenitic alloy that has an excellent resistance against common corrosion and pitting corrosion in media containing chloride due to its well-balanced analysis and extraordinarily high chromium content. Also because of its very high strength and good workability, VDM 33 is a material that can be used for many applications in the chemical processing industry. This datasheet provides information on composition, physical properties, elasticity, and tensile properties. It also includes information on corrosion resistance as well as forming, heat treating, machining, and joining. Filing Code: Ni-786. Producer or source: VDM Metals GmbH (a subsidiary of Acerinox, S.A.).

Sustainable and Resilient Capacity Expansion Planning for Chemical Processing Industry: A Bi-Objective Approach

According to United Nations reports, the worldwide population is expected to reach around 9.6 billion by 2050. This forecast emphasizes the critical role of energy and raw materials that are needed to meet the tremendous demand for goods. Consequently, firms feel pressured to establish sustainable and resilient strategic plans to acquire new process technologies and expand their capacity on time. These decisions are made at the highest management level, supported by a set of capacity expansion portfolios, to improve their competitiveness, especially in capital-intensive industries such as chemical processing. This paper investigates the sustainable and resilient capacity expansion problem for such industries within a long-term horizon. The main objective is to develop a holistic capacity expansion planning framework that fits the chemical processing industries and can be used to generate resilient scenarios while enhancing their sustainability measures. To this end, a bi-objective mixed-integer programming model was developed to solve the sustainability–resilience–profitability dilemma. The results showed a controversial relationship between the profitability of capacity expansion investments and a company’s commitment to sustainability and resilience. Furthermore, capacity expansion decisions were shown to primarily depend on the importance assigned to maximize profit as a managerial choice. However, there is no clear trend in sustainability preferences based on the sustainability weighting choice.

Circular-arc array for the pulsed eddy current inspection of thermally insulated pipelines

The inspection of corrosion under insulation (CUI) has been identified as a significant challenge in the petroleum and chemical process industries. As some of the most effective strategies, pulsed eddy current (PEC) techniques have proved effective for the measurement of the CUI of pipelines. In this paper, we propose a circular-arc array (CAA) to improve the measurement efficiency for the PEC inspection of thermally insulated pipelines. Based on the PEC system model for inspecting the CUI of pipelines, the magnetic field distribution of the CAA with multiple excitors was investigated. It is shown that the coverage of induced magnetic field gets much larger than that of the single excitor to realize high-efficiency measurements. Moreover, a sparsely distributed receiver array is designed to further improve the signal-to-noise ratio by eliminating the waviness effect due to multiple excitors. Finally, experiments were conducted, and the results demonstrated the effectiveness of the proposed method for the inspection of thermally insulated pipelines.