Chemical Plant Research Articles

Environmental Assessment of Nitrogen Compounds in the Surroundings of a Fertilizers Industrial Plant

"The present study was conducted in the proximity of a chemical plant from Târgu-Mureș (Romania), where nitrogen-based fertilizers were produced. The quality of the environmental components was assessed in terms of general physico-chemical parameters and the content of nitrogen dissolved ions (NO3-, NO2-). The nitrate content in soil varied depending on the distance from the source and land use at sampling point. The nitrite content was correlated with possible transformations from the nitrate forms. For water, special attention should be paid to the nitrate content from the industrial effluent, which in one sample proved to be higher than the national legislation. Keywords: nitrogen-based fertilizers, surface water pollution, soil pollution"

A high-precision and wide-range pH monitoring system based on broadband cavity-enhanced absorption spectrum

A high-precision pH monitoring system over a wide pH range is introduced. The system comprises a cavity-enhancement module constructed by two high-reflectivity mirrors, a microfluidic pH sensing chip based on a binary-indicator membrane of Congo red and m-cresol purple, and a hyperspectral transmission module. This structure extends the effective absorption optical path of the sensing chip, significantly amplifying the spectral differences at various pH values. The spectrum of the transmitted light is recorded by a self-developed hyperspectral module and then converted to broadband cavity-enhanced absorption spectrum (BBCEAS) via the Beer-Lambert law. An artificial neural network (ANN) is employed to predict pH values of the solution. With such a design, this system exhibits a wide detecting range of 2 M [H+] − 2 M [OH–] (corresponding to pH −0.3–14.3) with a response time of about 120 s. The system can achieve a higher detection accuracy with root mean square error (RMSE) of 0.073, as compared to 0.137 without the cavity enhancement. The system also possesses good properties of repeatability, long-term stability, ion resistance, and organic corrosion resistance. These excellent properties make the proposed system a promising candidate technology for harsh environments, such as seawater acidification warning, chemical plant sewage monitoring, and biological sample detection.

On-line Detection of Pressure Vessel Based on Infrared Imaging Technology

This paper introduces the application of infrared thermal imaging detection technology in the field of special pressure equipment, and applies infrared imaging detection to burners, low-temperature vessels, and tower vessels in chemical plants. The conclusion is that infrared detection technology can accurately detect temperature-maintaining defects in burners and low-temperature vessels, and can intuitively reflect the defect location in a high-heat form. For high-temperature equipment, the infrared characteristic form of insulation defects is generally displayed in an irregular chain shape, because the insulation is made up of materials that are spliced together and easily form pores at the joints. For low-temperature equipment, the humid environment caused by cold running may cause corrosion to exposed metals and even affect the process’s effectiveness. Infrared detection technology can visually reflect the location and degree of cold running, reduce equipment usage risks, and provide size and position guidance for insulation repair.

Handling Data Handoff of AI-Based Applications in Edge Computing Systems

Edge computing aims at better supporting low-latency applications. One of its key techniques is computation offloading, the process that outsources computing tasks from resourced-constrained mobile devices and moves them to edge data centers. In this paper, we tackle an emerging problem within the umbrella of computation offloading, i.e., migration of offloaded inference tasks of Artificial Intelligence (AI) trained models. Such context tailors migration aspects of data-sensitive services where i) the value of the updates is inversely proportional to the data age and ii) outage is highly detrimental to accuracy. To tackle this challenge, we propose Mobile Edge Data-handoff (MED) a framework able to relocate inference or online training tasks from one edge datacenter to another by moving only the necessary data to minimize any accuracy drop during the process. We implemented MED in a well-known edge computing emulator, openLEON, and experimentally verified its performance with an AI-based Industry 4.0 application that forecasts the gas flow in a chemical plant. For our experiments, we use a real, open-source dataset that contains sensors readings. Collected results show that MED, employing proactive data handoff algorithms, is able to minimize the packet loss during the handoff thereby providing guarantees on the inference accuracy.

Integrated process safety and process security risk assessment of industrial cyber-physical systems in chemical plants

Aligned with the development needs of Industry 4.0, industrial cyber-physical systems (ICPSs) are widely applied to chemical facilities to facilitate so-called intelligent production processes. Meanwhile, emerging cyber-to-physical (C2P) risks are introduced due to the vulnerability of ICPSs to cyberattacks. An integrated safety and security risk assessment of chemical facilities equipped with industrial cyber-physical systems becomes challenging, particularly in performing a probabilistic/quantitative risk assessment. Targeting this gap, this study develops a systematic approach to construct accident scenarios concerning both safety hazards and security threats and performs a probabilistic risk assessment of chemical facilities considering the interdependency between safety-associated events and security-associated events. In the proposed approach, bow-tie technique is used to perform a safety risk analysis, and meanwhile, the possible dangerous scenarios caused by physical attacks and C2P attacks are also identified and integrated into the bow-tie diagram. Particularly, attack impact modeling of C2P attacks helps to identify dangerous attack modes, and a time-to-compromise (TTC) based method is used to quantify the vulnerability of ICPSs to C2P attacks. Then, a Bayesian network (BN) model is developed to perform an integrated safety and security risk analysis. An illustrative case study is used in this study to give guidance on performing integrated safety and security risk assessment of ICPSs and validate the feasibility of the proposed approach.

Fault diagnosis-based SDG transfer for zero-sample fault symptom

The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a new unseen fault class appears in the test set, but there is no training sample of this fault in the training set. Therefore, studying the unseen cause-effect problem of fault symptoms is extremely challenging. As various faults often occur in a chemical plant, it is necessary to perform fault causal-effect diagnosis to find the root cause of the fault. However, only some fault causal-effect data are always available to construct a reliable causal-effect diagnosis model. Another worst thing is that measurement noise often contaminates the collected data. The above problems are very common in industrial operations. However, past-developed data-driven approaches rarely include causal-effect relationships between variables, particularly in the zero-shot of causal-effect relationships. This would cause incorrect inference of seen faults and make it impossible to predict unseen faults. This study effectively combines zero-shot learning, conditional variational autoencoders (CVAE), and the signed directed graph (SDG) to solve the above problems. Specifically, the learning approach that determines the cause-effect of all the faults using SDG with physics knowledge to obtain the fault description. SDG is used to determine the attributes of the seen and unseen faults. Instead of the seen fault label space, attributes can easily create an unseen fault space from a seen fault space. After having the corresponding attribute spaces of the failure cause, some failure causes are learned in advance by a CVAE model from the available fault data. The advantage of the CVAE is that process variables are mapped into the latent space for dimension reduction and measurement noise deduction; the latent data can more accurately represent the actual behavior of the process. Then, with the extended space spanned by unseen attributes, the migration capabilities can predict the unseen causes of failure and infer the causes of the unseen failures. Finally, the feasibility of the proposed method is verified by the data collected from chemical reaction processes.

Correlation between Soil Chemical Properties and Plant Mineral Elements for Nutrient Management of Mulberry (Morus alba L.)

Correlation between Soil Chemical Properties and Plant Mineral Elements for Nutrient Management of Mulberry (Morus alba L.)

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

In order to diagnose abnormal trends in the process parameters of industrial production, the Expert System based on rolling data Kernel Principal Component Analysis (ES-KPCA) and Support Vector Data Description (ES-SVDD) are proposed in this paper. The expert system is capable of identifying large-scale trend changes and abnormal fluctuations in process parameters using data mining techniques, subsequently triggering timely alarms. The system consists of a rule-based assessment of process parameter stability to evaluate whether the process parameters are stable. Also, when the parameters are unstable, the rolling data-based KPCA and SVDD methods are used to diagnose abnormal trends. ES-KPCA and ES-SVDD methods require adjusting seven threshold parameters during the offline parameter adjustment phase. The system obtains the adjusted parameters and performs a real-time diagnosis of process parameters based on the set diagnosis interval during the online diagnosis phase. The ES-KPCA and ES-SVDD methods emphasize the real-time alarms and the first alarm of process parameter abnormal trends, respectively. Finally, the system validates the experimental data from UniSim simulation and a chemical plant. The results show that the expert system has an outstanding diagnostic performance for abnormal trends in process parameters.

Kryviy Rih as the center of heavy industry of the USSR: the role of Kryvbas scientists in the development of the mining industry

The article completes the cycle of investigations within the framework of the historical and foundational research of the State Printing Archive "Printed editions of the 1970s: thematic and content priorities", planned for 2023. The object of the research was the priority topics that were covered in domestic printed editions of the specified period , the subject of which is the periodical and continuing editions published in Kryvyi Rih during the 1970s, namely: the yearbook "Mining ore production", published by the Scientific and Research Mining and Ore Institute (SRMOI) of Kryvyi Rih, and materials of all-Union and republican scientific and technical forums (seminars, conferences, etc.). Relying on the specified primary source base, the author considered the main directions of development of mining science in Kryvyi Rih during the researched period. The purpose of the proposed exploration is to form conclusive generalizations about today's little-known local prints, conduct a content analysis of their content, update important historical events from the past of Kryvyi Rih and personify the mining science of the Kryvyi Rih iron ore basin. Having achieved the set goals, the author claims that in the 1970s, Kryvyi Rih became an all-Union recognized center of scientific and practical mining. In the city, despite its modest (district) administrative-territorial status, powerful scientific schools were formed, higher, secondary special and vocational-technical mining education flourished. Having a closed cycle: from mining (Kryvyi Rih mines, quarries) and beneficiation of iron ore (five mining and beneficiation plants operate on the territory of the city) to the production of the best types of iron and steel (Metallurgical Plant "Kryvorizhstal", Kryvyi Rih Coke Chemical Plant) — Kryvyi Rih became the industrial pearl of the Soviet Union. In modern Ukraine, the city has rightly earned the name "iron heart of the state".

Application of Smart Overpressure Protection Devices to batch and semi-batch processes: The zero-emission mode

Chemical reactors in batch- and semi-batch processes are mainly protected by mechanical safety devices or safety integrity systems against critical runaway reactions. Both devices have in common, to limit with their fixed set conditions and discharge areas the productivity and flexibility of chemical plants. Besides, the trend in the industry is to reduce/avoid toxic and explosives to be discharges trough safety devices due to the increased requirements for climate- and environmental protection. A new idea in that regard is to connect the advantages of mechanical safety devices and safety integrated systems in so called smart overpressure protection devices (SmOP). Here, a predictive CSE conversion model (PreCoM) is online tracking the hazard potential of a chemical reaction and defines individual shutoff limits for the reaction. Its zero-emission mode ensures flexible, reaction independent application without fluid discharge. The smart relief mode is maximizing the plant utilization with individual shutoff limits and is minimizing the pollution with controlled discharge areas. In this article, the SmOP concept and the utilization of the PreCoM model for two different reactions and three industrial use cases for zero-emission application is being presented.

Influence of micronutrient fertilizers and growth regulators on soybean yield under the conditions of Primorsky Kray

Relevance. In 2021–2022, in Primorsky Krai (Timiryazevsky village), the influence of foliar top dressing Zn, Cu, Mg in chelated form («Terra Master» LLC, Russia), monokalium phosphate («Buisk Chemical Plant», JSC) was studied on the experimental fields of the Department of Agriculture and Agrochemistry of the A.K. Chaika Federal State Research Center for Agrobiotechnologies of the Far East, Russia), B (boric acid) («Delta» LLC, Russia), liquid micronutrients («Agro Expert Group» LLC, Russia), Boro-N, Fertix B, Potassium Lignohumate B for crop formation and protein content in soy beans of the Breeze variety.Methods. The treatment of crops with micro fertilizers was carried out in the phase of «2–3 triple leaves» and in the phase of «flowering» in the recommended norms.Results. It was found that the use of non-root fertilizing affected economically valuable signs. The highest yield when applying non-root fertilizing was noted in the variant with the introduction of Zn 31.2 c/ha in the phase «2–3 triple leaves», Boron-H 37.0 c/ha in the phase «flowering». Non-root top dressing had a positive effect on the accumulation of protein in soy grain. The maximum protein collection from 1 ha was obtained when using Boro-N in the flowering phase – 2.96 t/ha.

THE IMPORTANCE OF BREEDING PROGRESS IN WINTER WHEAT PRODUCTION

The impact of breeding on winter wheat yields and the use of the generated yield potential in production were assessed. The results of trials of Research Centre for Cultivar Testing (COBORU) from 1966-2022, data from the Seed Inspection (IN) from 1986-2001 and Plant Health And Seed Inspection (PIORiN) from 2002-2023 regarding the varietal structure of seed production of winter wheat and production data on wheat yield in production were used according to Statistics Poland. To assess the effects of breeding, the method of changing the yield potential of varieties was used. In this method, we use indexes determining the yield-producing capacity of individual varieties in relation to the standard. Changes in yield trends in variety experiments and production were analyzed. The growing importance of the variety as a factor enabling further increase in yield and the effect of breeding on the increase in yield potential has been demonstrated despite limitations in the use of nitrogen fertilization and chemical plant protection products. The existing yield gap between trials and production, amounting to approximately 55%, indicates great opportunities to further increase yields in production through better use of the yield potential of varieties created by breeding.

A comparative study of coal fly and bottom ashes as sustainable electroactive vibration damping materials

Coal is used in most of the power and chemical plants to meet energy needs which produce various waste ashes. Reuse of these ashes as electroactive materials has great importance for sustainable development. In this study, it was detected that the main components of coal fly (CFA) and bottom ashes (CBA) were oxides of silica (SiO<sub>2</sub>), iron (Fe<sub>2</sub>O<sub>3</sub>), aluminium (Al<sub>2</sub>O<sub>3</sub>), and magnesium (MgO), besides carbon. These are well-known electrorheological (ER) active materials. The aim of this study is to reveal dielectric and electroactive vibration damping capabilities of CFA and CBA. According to the dielectric and ER flow tests carried out in insulating silicone oil (SO), the optimum concentration of particles was determined to be 35 wt.% for both ashes. Higher ER yield stress (τ<sub>y</sub> = 135 Pa), higher ER efficiency (32.8), and better viscoelastic properties (τ<sub>c</sub> = 128 Pa, G' = 680 kPa) under 3.0 kV mm<sup>-1</sup> applied electric field were obtained for 35CFA/SO suspension system compared to 35CBA/SO (τ<sub>y</sub> = 125 Pa, ER<sub>eff</sub> = 24.0, τ<sub>c</sub> = 55 Pa, G' = 260 kPa). Although it was concluded that both ashes can be upcycled to sustainable and smart vibration damping alternative materials, better performance was observed for CFA particles.

Ingevity to close Louisiana pine chemical plant

Ingevity to close Louisiana pine chemical plant

Distribution of select per- and polyfluoroalkyl substances at a chemical manufacturing plant

Per- and polyfluoroalkyl substances (PFAS) are used in various industrial products; however, they pose serious health risks. In this study, soil, soil gas, and groundwater samples were collected at a PFAS manufacturing facility in New Jersey, USA, to determine the presence and distribution of PFASs from the soil surface to groundwater and at various distances from the presumed source. Fluorotelomer alcohols (FTOHs) were detected in soil (< 0.26–36.15 ng/g) and soil gas (160–12,000 E µg/m3), while perfluorinated carboxylic acids (PFCAs) were found in soil (4.3–810 ng/g), soil gas (<0.10–180 µg/m3), and groundwater (37–49 µg/L). FTOH and PFCA concentrations decreased as the distance from the presumed source increased, suggesting that PFCAs are likely to migrate in groundwater, whereas FTOHs primarily move in the vapor phase. The presence of PFAS in the groundwater, soil, and soil gas samples indicate its potential for vapor intrusion; thus, some PFAS may contribute to indoor air inhalation exposure. To the best of our knowledge, this is the first report on the quantification of volatile PFAS in soil gas at a PFAS manufacturing facility.

Dynamic risk assessment of chemical process systems using the System-Theoretic accident model and process approach (STAMP) in combination with cascading failure propagation model (CFPM)

To maintain continuous production, chemical plant operators may ignore faults or handle faults online rather than shutting down process systems. However, interaction and interdependence links between components in a digitalized process system are substantial. Thus, faults will be propagated to downstream nodes, potentially leading to risk accumulation and major accidents. However, limited attention has been paid to this type of risk. To model the risk accumulation process, a dynamic risk assessment method is proposed by integrating the system-theoretic accident model and process approach (STAMP) and the cascading failure propagation model (CFPM). Firstly, STAMP is used to model and analyze the system safety of a process system. Two CFPMs are then proposed to measure risk accumulation under two different engineering situations. The proposed method is applied to the Chevron Richmond refinery crude unit and its associated upstream process. The results show that the proposed approach can effectively quantify the process of risk accumulation. This method can generate a real-time dynamic risk profile to support auxiliary decision-making.

Multi-target regression via target combinations using principal component analysis

Data-driven methods have become increasingly widespread in the chemical industry; however, these methods require sufficient data for effective implementation. Moreover, collecting sufficient data can be challenging for new processes, products, batch processes, and other scenarios. To address this limitation, we propose a novel method called the principal component analysis target combination method, combining the Random Linear Target Combinations (RLC) and Principal Component Analysis. The proposed method utilizes the relationships of multiple inspections obtained in a single production cycle to improve the prediction accuracy using the Multi-Target Regression (MTR) approach. Although collecting multiple samples can be time-consuming and expensive, conducting several inspections on a single sample is relatively easy. In chemical products, multiple inspections are usually performed, and relationships, such as trade-offs, between these inspections exist. We first evaluated the effectiveness of the proposed method using 12 diverse real-world benchmark datasets frequently used in MTR tasks. The results show that the proposed method improves the average Relative Root Mean Squared Error by 3.16 % compared to single-target regression (Gradient Boosting) and improves by 2.0 % compared to RLC, upon which the proposed method relies. Subsequently, we evaluated the method on a real dataset from our company comprising formulation-design data for developing new elastic sealant products. The proposed method outperformed the other methods, confirming its effectiveness and ability to utilize multiple inspection data in chemical plants.

Identifying vulnerabilities of industrial control systems using evolutionary multiobjective optimisation

In this paper, we propose a novel methodology to assist in identifying vulnerabilities in real-world complex heterogeneous industrial control systems (ICS) using two Evolutionary Multiobjective Optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well-known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these vulnerabilities to generate combinatorial attacks. The generated attacks were aimed at compromising the safety of the system and inflicting economic loss. Results were compared against random attacks, and the performance of the EMO algorithms was evaluated using hypervolume, spread, and inverted generational distance (IGD) metrics. A defence against these attacks in the form of a novel intrusion detection system was developed, using machine learning algorithms. The designed approach was further tested against the developed detection methods. The obtained results demonstrate that the developed EMO approach is a promising tool in the identification of the vulnerable components of ICS, and weaknesses of any existing detection systems in place to protect the system. The proposed approach can serve as a proactive defense tool for control and security engineers to identify and prioritise vulnerabilities in the system. The approach can be employed to design resilient control strategies and test the effectiveness of security mechanisms, both in the design stage and during the operational phase of the system.

Multivariate alarm systems to recognize rare unpostulated abnormal events

AbstractMost chemical and manufacturing plants have safety/reliability systems in place that are well equipped to handle commonly occurring postulated abnormal events, but often prove to be ineffective in predicting highly unanticipated and randomly occurring unpostulated abnormal events. In this paper, an advisory system is developed to analyze and recognize such events, consisting of novel, multivariate alarm systems and response actions introduced using process modeling and path‐sampling for unpostulated abnormal events. It augments existing safety/reliability systems, suggesting actions when unanticipated abnormal events are approached. Forward‐flux sampling (FFS), developed to discover rare molecular dynamics pathways, is applied. With an approximate process model, for an exothermic continuous‐stirred tank reactor using a perturbed feed concentration, the FFS algorithm is applied to identify rare trajectories between high‐conversion and low‐conversion steady‐states, with key process variables saved at various “crossing points.” Then, committer probabilities, , are computed at each crossing point, yielding a mathematical model that expresses as a function of the key process variables; that is, the reactor temperature (T), cooling‐water flow rate (), and cooling‐water temperature (), selected as choices for the primary alarm variables. For these, alarm thresholds; that is, L—low, LL—low low, and LLL—low low low, are suggested by computing their critical ranges, given the ranges for every alarm threshold.

Ecological Responses of Soil Bacterial Communities to Heavy Metal Contamination Surrounding a Typical Coal-based Industrial Region

Microbial communities in the soil might be affected by heavy metal contamination caused by anthropogenic activities associated with the coal-based industry. This study analyzed the differences in soil physicochemical properties, heavy metal concentrations, and enzyme activities surrounding different coal-based industrial fields(coal mining industry, coal preparation industry, coal-based chemical industry, and coal-fired power industry) in Shanxi Province, North China. Moreover, soil samples from farmland and parks away from all the industrial plants were collected as references. Based on the 16S rRNA high-throughput sequencing, we identified the composition of soil bacterial communities. Spearman correlation and redundancy analyses were used to explore the relationships between soil bacterial communities and environmental factors. The results showed that the concentrations of most heavy metals were greater than the local background values, particularly for As, Pb, and Cd, but they did not exceed the risk screening values of Soil Environment Quality:Risk Control Standard for Soil Contamination of Agriculture Land(GB 15618-2018). There were significant differences in soil cellulase and alkaline phosphatase activities among sampling fields. Actinobacteria was the predominant bacterial phyla, with the highest relative abundance surrounding the coal-based chemical plants, followed by Proteobacteria. The soil bacterial communities were significantly affected by Cd, total carbon, total nitrogen, and alkaline phosphatase activity. This study could provide a foundation for the ecological remediation of the coal-based industrial region in the future.