Petrochemical Plants Research Articles

Analytical Study on Fire Temperature Distribution Characteristics in Petrochemical Plant Facilities

In petrochemical plant facilities handling highly inflammable and combustible substances, it is necessary to preferentially review the frame type (closed or open) of the facility with consideration to the various fire conditions that may arise depending on the substances being handled. Accordingly, a fire simulation was carried out in this study for different fire conditions by assuming three frame types, and the spatial temperature distribution was analyzed for each condition. Various temperature distributions were found according to the fire type, fire source, and frame type of the facility. The results showed that petrochemical plant facilities handling diverse substances need to implement fire-resistant designs appropriate for the purpose of the facility by comprehensively considering the arrangement and frame type of the facility and the fire conditions that are likely to arise.

Using artificial intelligence to find design errors in the engineering drawings

AbstractArtificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule‐based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI‐based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost‐benefit analysis and potential scale‐up of the developed software. Our goal is to share the successful experience of AI‐based product development that can substantially reduce the engineering hours and, therefore, reduce the project's overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry.

Sulfolane degrading bacteria from petrochemical plant: activated sludge enrichment, isolation and characterization

Sulfolane is extensively used as an extractive solvent in sour-gas processing plants in the petrochemical industry. After repeated use, deteriorated sulfolane becomes corrosive and may leak into the environment to threaten aquatic and terrestrial organisms or cause shock loading of wastewater treatment systems. It is therefore important to remove sulfolane from the environment and maintain stable wastewater treatment efficiency. To address this issue, bioaugmentation provides a solution by adding specific microorganisms into the biological treatment system to speed up the contaminant degradation rate. Isolation of microbes capable of degrading target contaminant is key point. Understanding the physiological characteristics of isolated microbes is indispensable for subsequent successful applications. In this study, sulfolane degrading bacteria from the activated sludge of a petrochemical plant were enriched using 200–1000 mg L−1 sulfolane and then isolated. After confirming their sulfolane degrading ability, sulfolane biodegradation was then investigated under various pHs, sulfolane and sulfate concentrations. Enrichment could enhance the sulfolane degrading rate of the mixed culture from 3.9 to 7.1 times. Three strains (strain Y-a, Y-d and Y-f) capable of degrading sulfolane were isolated. The degrading microbes were identified as Cupriavidus sp. using 16S rDNA sequencing. Sulfolane concentration biodegraded by strain Cupriavidus sp. Y-d at pH of 7–9 was 1.40–1.45 times higher than that at pH of 6. The optimal pH for strain Cupriavidus sp. Y-d to degrade sulfolane was 8. When increasing the sulfolane concentration from 500 to 2000 mg L−1, the specific growth rate and specific substrate utilization rate increased from 0.19 to 0.48 d−1 and 0.48 to 0.59 d−1, respectively. Strain Cupriavidus sp. Y-d was capable of degrading 2000 mg L−1 sulfolane. Sulfate concentrations higher than 2511 mg L−1 had a negative effect on sulfolane biodegradation.

Increasing biodegradability of a real amine-contaminated spent caustic problematic stream through WAO and CWAO oxidation using a high specific surface catalyst from petcoke

Different operating conditions of wet air oxidation and catalytic wet air oxidation have been studied for the treatment of highly concentrated methyldiethanolamine wastewater streams from amine units of acid gas recovery in petrol refineries. These units occasionally generate streams of high methyldiethanolamine content that require special actions to avoid undesirable impacts on the downstream biological process of the petrochemical wastewater treatment plant due to its inhibition effect. The wet air oxidation treatment achieved remarkable removals of methyldiethanolamine, sulfides, chemical oxygen demand and total organic carbon (99%, 95%, 65% and 38%, respectively). Likewise, activated petroleum coke materials from the own refinery plant were tested as catalysts in the process. These materials were prepared under different conditions (chemical activating agent and thermal carbonization process). The catalytic wet air oxidation treatment using an activated petroleum coke was able to remove the methyldiethanolamine at milder operation conditions keeping a similar performance in terms of wastewater treatment removals as compared to the non-catalytic experiments. This technology significantly increased the biodegradability of the treated effluents ranging from 25 to 70 % due to the formation of more biodegradable substrates (acetic acid and ammonium) for further biological treatment.

Environmental Noise Monitoring and Assessment of Petrochemical Plants

This paper will look at the Scottish regulatory guidance for the monitoring and assessment of environmental noise from petrochemical plants. The practical challenges of on-site measurement and long-term monitoring will be discussed. The use of BS 4142 and NANR 45 within this context and the appropriate data analysis to assess noise impact on residential locations.

Cover Picture: ChemBioEng Reviews 1/2023

ChemBioEng ReviewsVolume 10, Issue 1 p. 1-1 Cover PictureFree Access Cover Picture: ChemBioEng Reviews 1/2023 First published: 11 February 2023 https://doi.org/10.1002/cben.202370101AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Oil Refinery, Chemical & Petrochemical plant. Copyright: zorazhuang Volume10, Issue1February 2023Pages 1-1 RelatedInformation

From the selection of priority pollutants to the analysis of health data in the SENTIERI Project

evaluation of mortality and hospitalization of residents in five Italian National Priority Contaminated Sites, studied in the SENTIERI Project, for those pathologies which, on the basis of the assessments of international agencies and bodies, are known to have an aetiological connection with exposure to specific environmental pollutants. ecological study. SETTING AND PARTICIOANTS: resident population in the sites of Trieste and Piombino (steel plants), and Falconara, Livorno, and the industrial area of Milazzo (petrochemical plants and/or refineries). Data extracted from the Sixth SENTIERI Report. mortality (2013-2017) and hospitalization (2014-2018) for associated causes by exposure to specific pollutants detected in the soil and water matrices. the results obtained do not show common patterns between the excesses found in Trieste and Piombino Sites (steel plants) nor among Livorno, Falconara, and Milazzo (petrochemicals and/or refineries). Livorno and Trieste sites, having a larger population, show the greatest number of excesses. the proposed approach can be a useful tool, in addition to others, for the study of the health profile of residents in contaminated sites, being also the basis for aetiological epidemiological studies.

Three pathways towards elimination of CO2 emissions from industrial plants that use hydrocarbon fuels

Many industrial plants burn hydrocarbon fuels to generate electricity and high-temperature thermal energy required in their processes. This work proposes a novel system for generation of such thermal energy while eliminating CO2 emissions. The system produces electricity, thermal energy, methanol, and carbon black. Combined heat and power (CHP) unit generates high-temperature thermal energy and electricity required by the industrial plant; CO2 produced by CHP and by the plant itself is converted to methanol by using hydrogen produced via pyrolysis of methane, which has recently advanced to the first industrial scale plant. Thermal energy contained in the CHP flue gas replaces utility plant boilers or furnaces of the industrial plant (e.g. a refinery or a petrochemical plant). Two pathways are based on two different methane pyrolysis methods and CO2 removal via MEA absorption. The third pathway replaces MEA absorption with a novel in situ CO2 conversion reactor. Proposed pathways are examined as ways of eliminating CO2 emissions from a 5 million ton of crude oil per annum refinery. The study includes operational costs, capital costs, and material prices in USA, EU, and China. The results show that the proposed pathways are profitable for USA and EU, while for China they are not. The proposed pathways provide CO2 free energy generation from natural gas as an alternative to energy generation from either solar, wind or hydro energy sources. In addition, they enable capture and conversion of CO2 emissions from industrial process units.

Seasonal characterization, sources, and source-specific risks of PM2.5 bound PAHs at different types of urban sites in central China

Polycyclic aromatic hydrocarbons (PAHs) have been extensively investigated in China. However, most previous studies only focused on specific locations. Here, we conducted field studies simultaneously to explore the seasonal characterization, sources, and source-specific risks of 16 PAHs in four polluted cities in China. Similar seasonal Σ16 PAHs variations are observed in all four sites with relatively high values in winter and low values in summer and the ratios of Σ16 PAHs between winter and summer range from 2.7 to 4.4. From summer to winter, the extent of PAH increase outpaces that of PM2.5 increase resulting in significant increase of ∑16 PAHs/PM2.5 in winter. Five potential sources were resolved by PMF: (1) coal combustion (32–52%); (2) vehicle emissions (19–29%); (3) evaporation (9–26%); (4) coke oven emission (11–15%); and (5) biomass burning (5–13%). The highest fraction of evaporation is found in the Luoyang site due to the presence of many petrochemical plants, the largest oil refinery in Henan and large oil storage tanks. The coking factor contributes the largest proportion in Anyang site because of many out of date inefficient coking facilities. The source-related risks in four sites are all higher than the USEPA guideline value (1.0 × 10−6) except for biomass burning. The largest total risks are observed at Luoyang site, slightly higher than those of other cities. Our results suggest that the PAHs still need to be concerned due to the complexity of sources and source emitted risks in different type of cities, and specific source control strategies should be target in different regions.

Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

PurposeFire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.Design/methodology/approachThis paper presents a flame smoke detection algorithm based on YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection in target detection and improve the model detection performance. The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.FindingsBased on the actual situation of flame smoke, the loss function and activation function of YOLOv5 model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on flame smoke. The improved network model is superior to the existing methods in running time and accuracy.Originality/valueAiming at the actual particularity of flame smoke detection, an improved flame smoke detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network. This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

Machine learning modeling and experimental study to forecast the pressure of Very High-Pressure (VHP) steam in an industrial steam cracking process

The steam cracking process converts hydrocarbons such as ethane and propane into ethylene, propylene and etc. In this study, a number of crucial features, which can affect the furnace output of Very High-Pressure steam (VHP), are studied. Thus, a set of experiments is conducted at a petrochemical plant to determine the effect of the coil’ outlet temperature of cracked hydrocarbon gas, the feed flow rate, dilution steam (DS), negative pressure in the furnace, and flow rate of injected water into steam on the pressure of VHP steam. In addition, the credibility of industrial data is confirmed using the Leverage method. It is found that a maximum VHP steam pressure for each feed flow rate is obtained when the ratio of dilution steam to the feed rate roughly equals 0.37. The mentioned ratio also controls the coke formation in the reactor coils. In the following, three effective machine learning methods are developed, based on 1077 industrial data samples covering a wide operating conditions' range, for estimating process' VHP steam pressure. The model built by radial basis function (RBF) shows sprier agreement with plant data with an average absolute relative deviation (AARD), standard deviation (SD), and R2 values of 0.11%, 0.79% and 99.33%, respectively, for total data points. It is revealed that the developed models are reliable for controlling the amount of VHP steam pressure by changing feed flow rates, the flow rate of injected water into steam, dilution steam, and the temperature of outlet cracked hydrocarbon gas. In addition, a sensitivity analysis is accomplished using Spearman's correlation coefficient based on the most accurate RBF model.

The pollution characteristics, source identification and health risks of multiple classes atmospheric SVOCs with complex emission sources of the petrochemical plant and other industries

SVOCs are ubiquitous air contaminants, whilst little was known about their environmental fates and sources in atmosphere in ambient areas around petrochemical and other industries. Herein, the levels of typical SVOCs, polycyclic aromatic hydrocarbons (PAHs), organophosphate flame retardants (OPFRs), polybrominated diphenyl ethers (PBDEs), phthalate acid esters (PAEs), and alternative plasticizers (APs), were investigated in atmosphere of residential areas around a petrochemical industry plant. The plant is bordering a developing metropolis, with highly developed modern industries and transportations. PAEs (128.7 ± 72.2 ng/m3) were found with the highest levels in atmosphere, followed by APs (19.1 ± 14.6 ng/m3), PAHs (13.6 ± 5.01 ng/m3), OPRs (3.89 ± 1.85 ng/m3), and PBDEs (0.08 ± 0.06 ng/m3). Seasonal variation was observed for SVOCs in the particulate and gaseous phases (p < 0.05), with higher concentrations of PAEs, APs, and OPFRs in summer than winter, whereas the opposite for PAHs. Chemical properties of compounds will influence the G-P partitioning of SVOC in the atmosphere. The sources of SVOCs in the study area are complex, and mainly from plastics manufacturing (20.9%), chemical additive application (46.5%), and petroleum combustion (32.9%). The estimated lifetime lung cancer risk of PAHs was moderate in the residential areas, and was higher in winter than summer. In contrast, the incremental lifetime cancer risk for di (2-ethylhexyl) phthalate inhalation was below the acceptable level. The petrochemical plant was one of the primary sources of PAHs in the investigated area, posing a nonnegligible risk to the surrounding residents. This study enhanced our understanding of the environmental fates and health risks of multiple classes of SVOCs in atmosphere with complex emission sources near the petrochemical plant and other industries.

PRELIMINARY DATA REGARDING BIOREMEDIATION OF SOILS POLLUTED WITH PETROLEUM HYDROCARBONS BY USING CYCLODEXTRINS

The main characteristics of the pollution caused by refineries and petrochemical plants is that the source of pollution is active, most of the time, for a short period of time, but has an important intensity, the pollutiong agent being usualy made up of limited pretoleum fractions. Also, in most of tha cases of accidental spills of petroleum products, the affected soil surface is much smaller than the contaminated surface of the first aquifer encountered by the pollution front. The crude oil resulting from the extraction, having a complex composition, acts on the soil depending on the amount, composition and properties of the organic and inorganic components contained. β-Cyclodextrin is a chemical substance with the molecular formula C42H70O35. Cyclodextrins were the first compounds studied in terms of complexation behaviour and catalytic properties, the objective being to mimic enzymes. The paper presents data concerning the soil physical, chemical and biological characteristics and chemical characteristics of crude oil that will be used in the Greenhouse experiment. Also, in the paper is achieved the state of the art for β-cyclodextrins used in bioremediation of soils polluted with petroleum hydrocarbons.

Plastics industry sees sustainable start in an Ohio plant

In Akron, Ohio, situated amid industrial buildings and auto service centers, is a small oasis of polished steel and nonslip scaffolding. The proprietors of this new facility hope it’s the future of the plastics industry . The facility, a pyrolysis plant run by Alterra Energy, breaks down plastic waste into a crude oil–like mixture that can be fed into petrochemical plants and transformed back into new plastics. The plant can process about 60 metric tons (t) of plastics a day. It was the largest operational plastics pyrolysis plant in the US until recently, when it was edged out by a larger facility that ExxonMobil started up in Houston. Many more pyrolysis plants, including ones using Alterra’s technology, are on drawing boards for sites around the world. Journalists visited the Alterra plant in November during a trip organized by the American Chemistry Council, a trade group keen on promoting plastics recycling

Study on Starting Scheme of Large Power Motor Transformer-Motor Set in Petrochemical Plant

Study on Starting Scheme of Large Power Motor Transformer-Motor Set in Petrochemical Plant

Эффективный экстрагент в технологии очистки фенолсодержащих сточных вод

Phenol and its derivatives are among the most dangerous pollutants entering the environment as part of wastewater from oil refineries and petrochemical plants, as well as pharmaceutical industry and construction materials. In surface water bodies, phenols occur in the form of their derivatives, namely: free phenols, phenolate ions and phenolates. The listed compounds have different degrees of toxicity, forming humus-like compounds as a result of polymerization and condensation reactions. For this reason, the treatment of phenolic wastewater is a very urgent problem in the petrochemical industry due to the high carcinogenicity of these impurities. Deep purification of water effluents is complicated by the fact that none of the known methods of removing phenols can achieve, with relatively acceptable technical and economic indicators, the required degree of purification. In accordance with the guidance document RD 52.24.480-2006 and the "Instructions for the formation and presentation of operational information on extremely high and high levels of surface water pollution, as well as their accidental pollution" (IGCE, Moscow, 2001), when assessing the pollution of natural waters with phenols, in all cases, 0.001 mg/dm3 is used as the MPC.

ИСПОЛЬЗОВАНИЕ ЦИФРОВЫХ ДВОЙНИКОВ ДЛЯ ЭКОЛОГИЗАЦИИ ХИМИЧЕСКОЙ ПРОМЫШЛЕННОСТИ

The article describes the potential benefits of implementing and using a digital twin of petrochemical plants as a virtual representation of the production process, including its technological parameters and real-time analytics to ensure an environmentally responsible approach to production. The authors point out the possibility of optimizing production by using sensors and automatic control systems, as well as using the digital twin for testing and refining assumptions. In addition, the implementation of a digital twin in a chemical plant, including with elements of AI, will allow for operational process management. The decision tree proposed by the authors allows a chemical enterprise to reduce its impact on the environment by using a decision support system to manage wastewater treatment at a petrochemical plant. Laborious calculations for forecasting modernization taking into account production, economic, and environmental factors can be obtained using a digital twin not only promptly but also with a specified degree of variation and reliability.

Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

On the issue of ensuring the stability and reliability of operation of continuous self-monitoring measuring systems for accounting pollutant emissions

The main purpose of the article is to develop an algorithmic software solution for an automated measuring system for accounting for emissions, capable of determining the conditions for the stability of measurements of input data for estimating the contents of the components of the chemical composition of pollutants, and allowing to replace incorrect data from a faulty measuring instrument with data from logical (indirect) measurements based on indications of serviceable measuring instruments. The results of using a multidimensional process disorder detector and a stable value reconstructor on historical data of a petrochemical process plant are demonstrated.

Transformation of oil scum as magnetic char for effective recovery of Cu and Ni from EDTA-bearing wastewater

Blackish ropy scum was collected from the scum vessel of the floatation tank from the petrochemical wastewater treatment plant.