Petrochemical Plants Research Articles

RBI-IOWs integrated approach to risk assessment: Methodological framework and application

The planning of inspection and maintenance activities is a challenge that researches are largely addressed in petrochemical plants. The Risk-Based-Inspection (RBI) methodology, defined by the API 580 and API 581 Recommended Practices, is the main tool widely used for planning inspection and maintenance activities. However, the validity of the RBI results is closely related to the operating conditions that are supposed to remain constant throughout the inspection period. Significant efforts were made to improve the basic RBI approach. Uncertainty in forecasting models, accuracy in the assumptions of degradation-based approaches, subjective judgment and human error in risk assessment, lack of compliance with industry codes and standards, need to improve maintenance planning and inspection interval optimization, remain the key challenges to be addressed.A tool that can allow a dynamic approach to updating the inspection plan, by a real-time readjustment of the variation in the equipment's risk levels, in compliance with the oil and gas reference standards, is the API 584 Integrity Operating Windows (IOWs) methodology, through which critical windows are defined for the parameters considered to have the greatest impact on the equipment integrity. The adoption of this methodology is capable of overcoming the aforementioned limitations of the RBI, because the inspection plans can be directly modulated by monitored variations in the critical process parameters of the system under analysis.With these premises, the aim of the present study is to outline a fully integrated dynamic risk-based methodological framework to enhance the basic RBI approach by using IOWs analysis, which provides the basis for the development of a procedural model to be implemented in the inspection planning practice. Inspection and maintenance departments are the main beneficiaries, being aware of changes or upset during an operation running, which could give significant advantage in the identification of a not expected accelerated damage mechanism, and its critical effects.To show its application in improving the monitoring of critical plant parameters, which could limit the reliability of pressure equipment during their life cycle, reference is made to the PACOL-HF plant section of the SASOL factory located in Augusta (Italy). As a main result, the proposed approach and its procedural model prove effective in preventing the occurrence of undesired events, through both a real-time monitoring of the process variables, and the resulting mitigation actions that can avoid the failure of the equipment.

A hybrid framework to analyze crisis management system maturity in sociotechnical systems

In recent decades, numerous catastrophic accidents have occurred worldwide, ranging from natural events (such as tsunamis, flooding, and earthquakes) to industrial events (such as mining and chemical process disasters). They endanger humans, the environment, organizations, and societies, as well as national security. However, there have been few attempts to propose a model for assessing the required capabilities and potential challenges in crisis management system maturity (CMSM). Accordingly, this study proposes a framework for measuring CMSM levels in complex systems. To this end, a CMSM taxonomy was developed, including aspects, dimensions, and factors influencing the CMSM. Fuzzy inference sets and fuzzy analytical hierarchy processes are then used for knowledge acquisition, quantification of the CMSM, and dealing with epistemic uncertainty. An actual complex petrochemical plant is investigated to evaluate its capabilities. The findings revealed the most significant contributing factors: the CMSM’s current capability and challenges level (score), as well as the capacity to overcome the potential crisis response challenges. Moreover, the proposed model can be used as a practical approach for various chemical-processing plants.

Process optimization, exergy efficiency, and life cycle energy consumption-GHG emissions of the propane-to-propylene with/without hydrogen production process

Propylene is an extreme important intermediate chemical and it is traditionally produced by steam cracking and fluid catalytic cracking of light oil in petrochemical plants, which will be gradually limited by the increasingly scare oil resources. The propylene production from propane is promising and expected to significantly reduce oil consumption. The process modeling and system analysis methods are used to investigate the techno-environmental performance of the propane dehydrogenation processes with poly-generation of propylene and hydrogen. The propane dehydrogenation processes have great energetic and environmental competitiveness compared with the coal-to-propylene. The propane consumption and direct CO2 emissions are only 1.19 t/t C3H6 and 415 kg/t C3H6. The exergy efficiency of the propane dehydrogenation process with no H2 production is calculated to be 77.61%, while that are as high as 78.68% and 80.73% for the propane dehydrogenation process with H2 recovery rates of 90% and 95%. After subtracting the hydrogen production part, the life cycle energy consumption and GHG emissions of 1 t propylene for the process with H2 recovery rate of 95% are 2.72% and 3.97% lower than that the process with no hydrogen production. This study confirms that development of propane-based propylene and hydrogen production is energetic and environmentally competitive and is therefore promising in olefins industry.

Dealloying of Ni- and Fe-Based Alloys in Boiling Caustic Environments and the Parameters Influencing the Geometry of a Dealloyed Layer

Nickel alloys and stainless have been the material of choice for a wide range of industrial applications like power and petrochemical plants that involve exposure to corrosive environments. For instance, Alloy 800 (Fe-30Ni-20Cr) has been used as the tubing material in CANDU and Siemens steam generators (SGs) for decades with excellent in-service performance. However, despite their good resistance to general to localize corrosion, Ni- and Fe-based alloys are susceptible to environmentally assisted cracking (EAC) in certain environments, such as hot caustic aqueous solutions. Laboratory experiments have shown that these materials are susceptible to dealloying in hot caustic environments due to the selective removal of Fe, Cr and Mo (less noble elements). As a result of dealloying, a brittle nanoporous film forms on the surface that is enriched in Ni (the more noble element). This film has been proposed to act as a precursor to stress corrosion cracking (SCC) by a film-induced cleavage (FIC) mechanism, where the brittle film injects a high speed/energy crack to the substrate material.The fast kinetics of dealloying requires a connected percolating pathway of less noble element atoms to exist within the alloy that extends from the surface into the material. The presence of this pathway depends on the composition of the alloy. The parting limit defines the minimum amount of the more reactive element(s) in a particular alloy system that makes it susceptible to dealloying. Below the parting limit composition, dealloying will be suppressed by surface diffusion of the more noble element, even at very oxidizing potentials, and the surface of the material will show a passive-like behavior (general dissolution would eventually occur at high anodic overpotentials). Besides the alloy composition, dealloying highly depends on the electrode potential. Below a threshold potential (dealloying critical potential), surface diffusion of the more noble element will overcome the dissolution of the less noble element(s) and suppress dealloying.In this study, we examine different factors that affect dealloying and the geometry of a dealloyed layer through electrochemical measurements and nano-scale characterization techniques. Several engineering Ni- and Fe-based alloys were tested in deaerated boiling caustic solutions to understand the effect of alloy composition on the susceptibility to dealloying. Results indicate that an increase in Ni content will shift the dealloying critical potential to more positive potentials and subsequently the resistance to dealloying. Although Ni is the alloying element that provides nobility and resistance in hot caustic environments, our results show that the thermodynamic and kinetic behaviour, and the content of the less noble elements play an important role in these environments. A comparison between different alloys with similar Ni-content but different Cr and Mo contents shows the beneficial and detrimental role of Cr and Mo in dealloying, respectively. The air-formed chromia-based oxide on the surface of alloys with high Cr-content slowly dissolves in the caustic environment, delays the dissolution of less noble elements, and increases the resistance to dealloying.Changes in electrode potential and time of exposure to the caustic environments, alter the geometry of the dealloyed layer. Results show an increase in the diameter and coarsening of ligaments with increasing time of exposure to the caustic environment. Coarsening of ligaments is believed to impair the ability of a dealloyed layer to inject a micro-crack to the underlying material and initiate SCC. Also, results show that an increase in anodic overpotential of only a few millivolts in the vicinity of dealloying critical potential would change the geometry of the dealloyed layer significantly. The surface diffusion of the more noble element (i.e. Ni) cannot keep up with the fast removal of atoms of the less noble elements (i.e. Cr, Mo, and Fe) at more positive electrode potentials compared to the dealloying critical potential which results in a weak bonding at the film/metal interface. The weak bonding may not be able to support the transmission of a high-speed crack to the substrate material, reducing the likelihood of SCC initiation. Figure 1

The implementation of discrete-event simulation and demand forecasting using Temporal Fusion Transformers to validate spare parts inventory policy for petrochemicals industry

One of the important strategic factors of petrochemicals plant maintenance is the spare parts inventory policy, It is significant for the efficiency, reliability, and productivity of the petrochemicals industry. An unsuitable spare part inventory policy will lead to a loss in long engineering machinery downtime due to a shortage of spare parts. To implement the spare parts inventory policy which is able to fulfill the future demand for spare parts, calculation by various statistical theories and working processes is used to custom the spare parts inventory policy. However, to validate whether or not the customized spare parts inventory policy is suitable, the discrete-event simulation library SimPy is used to mimic the actual spare parts inventory system. It must be involved in the performance evaluation process of the customized spare parts inventory policy. The inventory simulation model consists of many events depending on the supply chain system. The crucial event which is the most complex for the simulation of spare parts inventory is the demand event. This work applies the demand forecasting technique to the simulation by using deep learning with a prebuilt architecture model called Temporal Fusion Transformers (TFT). The averaged MAE of the point predictions from a global model is 0.4874+/- 6.7744 on the validation dataset and 0.6424+/-3.4963 on the test dataset. Our method predicts a quantile forecast of the future demand which is able to handle the stochastic nature of the spare parts demand in the petrochemicals industry and the result from the simulation outcome is more accurate and close to the outcome from an actual inventory system. The information from the analysis of the simulation outcome is used by the inventory management team to make decisions about the custom inventory policy before deploying it to the actual system.

The implementation of discrete-event simulation and demand forecasting using Temporal Fusion Transformers to validate spare parts inventory policy for petrochemicals industry

One of the important strategic factors of petrochemicals plant maintenance is the spare parts inventory policy, It is significant for the efficiency, reliability, and productivity of the petrochemicals industry. An unsuitable spare part inventory policy will lead to a loss in long engineering machinery downtime due to a shortage of spare parts. To implement the spare parts inventory policy which is able to fulfill the future demand for spare parts, calculation by various statistical theories and working processes is used to custom the spare parts inventory policy. However, to validate whether or not the customized spare parts inventory policy is suitable, the discrete-event simulation library SimPy is used to mimic the actual spare parts inventory system. It must be involved in the performance evaluation process of the customized spare parts inventory policy. The inventory simulation model consists of many events depending on the supply chain system. The crucial event which is the most complex for the simulation of spare parts inventory is the demand event. This work applies the demand forecasting technique to the simulation by using deep learning with a prebuilt architecture model called Temporal Fusion Transformers (TFT). The averaged MAE of the point predictions from a global model is 0.4874+/- 6.7744 on the validation dataset and 0.6424+/-3.4963 on the test dataset. Our method predicts a quantile forecast of the future demand which is able to handle the stochastic nature of the spare parts demand in the petrochemicals industry and the result from the simulation outcome is more accurate and close to the outcome from an actual inventory system. The information from the analysis of the simulation outcome is used by the inventory management team to make decisions about the custom inventory policy before deploying it to the actual system.

Assessment of Inhibitive Drugs for Corrosion Inhibition Applications in Petrochemical Plants – A Review

The world is paying more attention and trying to eliminate the use of substance that are toxic to human and the environment. The use of some organic and inorganic corrosion inhibitors exhibit toxicity and research has been ongoing on the use of eco-friendly substitutes. Plant extract, dyes and drugs have shown good corrosion inhibitive capabilities and this work gives a summary of several works that have been done on the use of drugs as efficient corrosion inhibitors for mild steel which is mostly used in the petroleum and petrochemical industry in acidic media such as HCl and H2SO4.

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning.

Due to the abrupt nature of the chemical process, a large number of alarms are often generated at the same time. As a result of the flood of alarms, it largely hinders the operator from making accurate judgments and correct actions for the root cause of the alarm. The existing diagnosis methods for the root cause of alarms are relatively single, and their ability to accurately find out complex accident chains and assist decision making is weak. This paper introduces a method that integrates the knowledge-driven method and the data-driven method to establish an alarm causal network model and then traces the source to realize the alarm root cause diagnosis, and develops the related system modules. The knowledge-driven method uses the hidden causality in the optimized hazard and operability analysis (HAZOP) report, while the data-driven method combines the autoregressive integrated moving average model (ARIMA) and Granger causality test, and the traceability mechanism uses the time-based retrospective reasoning method. In the case study, the practical application of the method is compared with the experimental application in a real petrochemical plant. The results show that this method helps to improve the accuracy of correct diagnosis of the root cause of the alarm and can assist the operators in decision making. Using this method, the root cause diagnosis of alarm can be realized quickly and scientifically, and the probability of misjudgment by operators can be reduced, which has a certain degree of scientificity.

Origin of fine needle-like M23C6 carbides in a heat resistant steel after extended service

Components in refineries and petrochemical plants exposed to extended service offer a unique opportunity to investigate the effect of a long heat exposure on the microstructure. Twenty years of service at 900 °C of a super austenitic Fe-0.5C-25Cr-35Ni-1Nb-0.1Ti alloy led to the formation of both coarse intergranular equiaxed as well as fine intragranular M23C6 needle-like carbides. Cube-cube and twin-like Σ3 orientation relationships are identified between the matrix and fine needle-like carbides. The former relationship maintains {111}CC//{111}M parallelism while the latter satisfies only one (111)TC//(111)M with 60° twist misorientation similar to that of twins or Σ3 in FCC crystals. Both planar relationships generate low energy {111} carbide-matrix phase boundaries. Cube relationship generates Σ3 phase boundary and a superlattice with 3 times lattice parameter of the matrix while twin-like relationship results in Σ9 phase boundary and a superlattice with 3√3 times lattice parameter of the matrix. The carbide growth direction is determined as 〈011〉C. All six <011>M directions in the matrix accommodate the growth of needle-like carbides resulting in an intricate 3D structure. Twinning is observed in some needle-like carbides resulting in coexisting cube-cube and twin-like orientation relationships with the matrix. Carbide twinning leads to three in-common <011>C directions for preferential growth. Twinning also allows needle-like M23C6 to form serrated cube-cube and straight twin-like {111} phase boundaries with the matrix.

The Research Application of 3D Laser Scanning Technology in the Deformation Detection of Large Cylindrical Oil Tank

In order to ensure the safety in using a large cylindrical storage tank, it is necessary to regularly detect its deformation. The traditional total station method has high accuracy in determining the deformation, however, it has a low measurement efficiency. Long-term observation means, there are more risks in the petrochemical plant, therefore, this paper proposes the usage of the 3D laser scanner, replacing the traditional total station to determine the deformation of a large cylindrical storage tank. The Matlab program, is compiled to calculate the point cloud data, while the tank deformation is analyzed from two different points which are, the local concave convex degree and the ovality degree. It is concluded that, the difference between the data obtained by 3D laser scanning, and total station is within the range of oil tank deformation limit, therefore, 3D laser scanner can be used for oil tank deformation detection.

Investigation of Naphtha Hydrotreating (NHT) Unit Exergy Utilization: A Case Study of a Petrochemical Industry in A developing Country

The need for efficient energy use in refinery operations is the proffered solution to the poor management of energy resources which has resulted in the instability in oil products prices, irregular plant shutdowns experienced in refineries of developing countries and increased carbon emission to the atmosphere. This research work aims to carry out exergy analysis on major energy consuming units which will aid in determining sites and causes of primary energy losses and proffer solutions to maximizing energy utilization. The Naphtha Hydro-treating (NHT) unit of the case model refinery was successfully simulated using Aspen Hysys. The analysis revealed more than half (51.9 %) of the input exergy was lost to the environment and an irreversibility of 32.7% occurred in the Stripper column. Comparison was also made with the same unit at Kaduna refinery and petrochemical plant and the results showed a conversion efficiency of about 91.28% when compared to 44.35% from the old refinery though this could be attributed to factors like age of plant, inadequate maintenance etc. The positive value of the amount of heat recovered from that of the flue gas in the boiler and furnace gave an opportunity to recover the energy lost and integrated back to the system for economic viability of the plant which result in a payback period that is less than a year.

Failure analysis of welded joint leakage of TP321 pressure pipeline

Considering the continuity of production in petrochemical plants and the harmfulness of leakage, any slight defect in the system may lead to dramatic accidents. Therefore, we used systematic characterization methods to analyze the causes of leakage near the weld of outdoor pressure pipe in a petrochemical plant. It is found that the crack extends from the outside into the inside surface of the pipe wall, and there are massive corrosion products on the crack surface. Furthermore, the damaged parts observed of the external insulating layer are wet and accumulate chlorine, which from the corrosive environment to the pipe material. Finally, it is concluded that microcracks originate from pitting pits and gradually expand to the inner surface with the coaction of internal pressure of the pipe up to 12 MPa. The reason of pipe leakage is the interaction of pitting corrosion, transgranular corrosion, and stress corrosion cracking.

Exploring the Resilience Park Index from the Perspective of Flood and Wind Disasters

At present, various climate disasters, global warming and other problems feed off of one another. Natural disasters such as earthquakes, typhoons, flooding, and drought as well as human-made disasters such as petrochemical plant safety accidents and steam explosion accidents pose a threat to and affect the development of cities. Therefore, scholars, scientists, and urban planners must study and discuss disasters, so as to work toward important concepts such as resilient cities, resilient landscapes, and disaster prevention parks. This study discusses the resilience park from the perspective of the resilient city. Since the harm caused by natural disasters to parks is mainly due to flooding and high winds, this study focuses on the prevention and control of flood and wind damage. This study is an attempt to explore the indicators of resilience of parks in the context of flood and wind damage, calculate the indicator weight of resilience parks, and establish an indicator evaluation system for resilience parks. The research methods of this study are the Fuzzy Delphi method and the network analysis program method. The results show that the indicators of a resilience park under flood disaster are different types of soil drainage capacity grade (0.199), the lawn to permeable ratio (0.248), the permeable pavement grade (0.125), and the degradation basin grade (0.177). The indicators of a resilience park under high winds are the wind resistance grade of tree species (0.064), the multilayer planting configuration grade (0.053), the tree height and wind resistance grade (0.066), and the bearing capacity grade of trunk and root system (0.067). The weight of each index is calculated. Finally, the park toughness index evaluation system is established. Through this study, we hope to put forward reconstruction suggestions for different parks and provide the evaluation criteria of park toughness so as to improve the disaster resistance of parks.

Experimental Study of Fire Damage Characteristics in a Petrochemical Plant Facility

As a part of the initial investigation of fire damage diagnosis, fire protection-applied specimens within petrochemical plant facilities were exposed to standard and hydrocarbon fire conditions; then, the heating temperature of the specimens was monitored, and their appearance was checked by visual observation. Although data on the appearance status for discoloration, thickness variations, etc. of fire protection were collated for each set of fire exposure conditions, it was difficult to estimate the heating temperature of the specimens. Therefore, the visual observation method for determining the degree of fire damage needs to be approached somewhat conservatively. Moreover, according to the results of the heating temperature measurements for each specimen, it was confirmed that the paint has a disadvantage in maintaining its fire resistance performance when exposed to a hydrocarbon fire.

Case-study of a flowsheet simulation using deep-learning process models for multi-objective optimization of petrochemical production plants

This work demonstrates a case-study to create machine-learning based process models from process data, which are connected into an overall process flowsheet and provide a high level of numerical stability for further multi-objective optimization. The used models are black-box and gray-box models, which are further compared to the proven LP approach. The proposed methodology is demonstrated using real-world measurement data from a refinery, involving a distillation unit, a hydrotreater, a reformer and an ethylene plant. The developed unit models are connected into an overall process flowsheet, which is solved by a sequential-modular approach and optimized in view of maximizing production margin and minimizing CO2-emissions. This work points out that the combination of engineering knowledge with data-driven techniques enables the incorporation of indirect information for process units, e.g., the crude oil composition vector for downstream units, leveraging the prediction performance of the unit models, compared to models not involving this information.

Air Pollution and Chronic Kidney Disease Risk in Oil and Gas- Situated Communities: A Systematic Review and Meta-Analysis.

Objective: This systematic review and meta-analysis aimed at synthesising epidemiological data on the association between long-term air pollution and kidney-related outcomes in oil and natural gas (ONG) situated communities. Methods: We synthesised studies using the PRISMA 2020 guideline. We searched databases including Medline, Cochrane Library, CIHANL, CAB s, Greenlife, African Journal Online, Google Scholar and Web of Science, from inception to April 2021. Heterogeneity across studies and publication bias were assessed. Results: Twenty-five studies were systematically reviewed but only 14 were included in the meta-analysis and categorised based on the outcome studied. Residents of exposed communities have increased risk for Chronic Kidney Disease (CKD) (OR = 1.70, 95% CI 1.44–2.01), lower eGFR (OR = 0.55, 95% CI 0.48–0.67) and higher serum creatinine (OR = 1.39, 95% CI 1.06–1.82) compared to less exposed or unexposed populations. The risks for hypertension and kidney cancer between the two populations were not significantly different. Conclusion: We report an increased risk for CKD and kidney dysfunction in populations residing near petrochemical plants, although from a limited number of studies. The scientific community needs to explore this environment and non-communicable disease relationship, particularly in vulnerable populations.

Design of Pile Foundation for Distillation Column

Abstract Equipment foundation is one of the most critical facility in every petrochemical, fertilizer, oil and gas plants. This equipment comprises distillation columns, vertical vessels, horizontal vessels, high pressure exchangers, reactors etc. Out of this equipment, distillation column is one of the most critical equipment due to its large diameter and height hence resulting into heavy dead load and heavy transverse loads at the base of column. Hence proper foundation system should be designed to cater all these loading without affecting the strength and serviceability of the foundation system. In most of the cases due to poor bearing capacity of soil one need to go for pile foundation. This paper summarizes the Indian code and process industry practices, design loads and load combinations to be adopted for design of pile foundation. One sample example is solved to evaluate the no of piles for column foundation by assuming suitable loading and vessel data.

Evaluation of high temperature corrosion resistance of CrN, AlCrN, and TiAlN arc evaporation PVD coatings deposited on Waspaloy

During the regular operation of a petrochemical plant, the degradation and corrosion of some metallic parts frequently occur. These parts are exposed, from the oil extraction to refining processes, to an environment that includes strong acids or bases, under high pressures, and high temperatures, up to 760 °C. Waspaloy (UNS N07001) is a material currently applied to manufacture components exposed to hot corrosion. This study evaluates the high temperature corrosion behavior of CrN, AlCrN, and TiAlN coatings deposited by Arc Evaporation Physical Vapor Deposition (PVD) process onto Waspaloy base material. The AlCrN and TiAlN PVD coatings showed higher hardness, 28 GPa (TiAlN) and 33 GPa (AlCrN), and elastic modulus than the CrN coating. The AlCrN coating presented the lowest corrosion rate values, 1.35 × 10−5 mg2·cm−4·s−1, while TiAlN and CrN showed higher corrosion rate values, 1.66 × 10−5 and 2.09 × 10−4 mg2·cm−4·s−1 under 700 °C hot corrosion condition. The use of X-ray diffractograms, and Raman spectroscopy, to evaluate the coating after hot corrosion tests based on chemical composition indicated that AlCrN and TiAlN coatings formed Al2O3 and TiO2 on the surface, which was responsible for their higher corrosion resistance.

In-Depth Characterization of Plant Growth Promotion Potentials of Selected Alkanes-Degrading Plant Growth-Promoting Bacterial Isolates.

The use of plant growth-promoting rhizobacteria (PGPR) as a bioremediation enhancer in plant-assisted phytoremediation requires several steps, consisting of the screening, selection, and characterization of isolates. A subset of 50 bacterial isolates representing a wide phylogenetic range were selected from 438 morphologically different bacteria that were originally isolated from a petroleum hydrocarbon (PHC)-polluted site of a former petrochemical plant. Selected candidate bacteria were screened using six conventional plant growth-promoting (PGP) traits, complemented with the genetic characterization of genes involved in alkane degradation, as well as other pertinent functions. Finally, the bacterial isolates were subjected to plant growth promotion tests using a gnotobiotic approach under normal and stressed conditions. Our results indicated that 35 bacterial isolates (70%) possessed at least four PGP traits. Twenty-nine isolates (58%) were able to utilize n-hexadecane as a sole carbon source, whereas 43 isolates (86%) were able to utilize diesel as the sole carbon source. The presence of catabolic genes related to hydrocarbon degradation was assessed using endpoint PCR, with the alkane monooxygenase (alkB) gene found in 34 isolates, the cytochrome P450 hydroxylase (CYP153) gene found in 24 isolates, and the naphthalene dioxygenase (nah1) gene found to be present in 33 isolates. Thirty-six strains (72%) promoted canola root elongation in the growth pouch assay. After several rounds of screening, seven bacterial candidates (individually or combined in a consortium) were tested for canola root and shoot growth promotion in substrates amended by different concentrations of n-hexadecane (0%, 1%, 2%, and 3%) under gnotobiotic conditions. Our results showed that Nocardia sp. (WB46), Pseudomonas plecoglossicida (ET27), Stenotrophomonas pavanii (EB31), and Gordonia amicalis (WT12) significantly increased the root length of canola grown in 3% n-hexadecane compared with the control treatment, whereas Nocardia sp. (WB46) and Bacillus megaterium (WT10) significantly increased shoot length compared to control treatment at the same concentration of n-hexadecane. The consortium had a significant enhancement effect on root length compared to all isolates inoculated individually or to the control. This study demonstrates that the combination of PGPR traits and the PHC degradation potential of bacteria can result in an enhanced beneficial effect in phytoremediation management, which could lead to the development of innovative bacterial inoculants for plants to remediate PHC-contaminated soils.

Carcinogenic and health risk assessment of respiratory exposure to acrylonitrile, 1,3-butadiene and styrene in the petrochemical industry using the US Environmental Protection Agency method

Objectives. This study aimed to assess carcinogenic and health risks of respiratory exposure to acrylonitrile, 1,3-butadiene and styrene (ABS) in the petrochemical industry. Methods. This cross-sectional study was conducted in a petrochemical plant producing ABS copolymers. Respiratory exposure to each of acrylonitrile, 1,3-butadiene and styrene was measured using methods No. 1604, No. 1024 and No. 1501 of the National Institute of Occupational Safety and Health (NIOSH), respectively. The US Environmental Protection Agency (USEPA) method was used to assess carcinogenic and health risks. Results. The average occupational exposure to ABS was 560.82 µg m–3 for 1,3-butadiene, 122.8 µg m–3 for acrylonitrile and 1.92 µg m–3 for styrene. The average lifetime cancer risk in the present study was 2.71 × 10−3 for 1,3-butadiene, 2.1 × 10−3 for acrylonitrile and 6.6 × 10−3 for styrene. Also, the mean non-cancer risk (hazard quotient) among all participants for each of 1,3-butadiene, acrylonitrile and styrene was 4.04 ± 6.93, 10.82 ± 14.76 and 0.19 ± 0.11, respectively. Conclusion. The values of carcinogenic and health risks in the majority of the subjects were within the unacceptable risk levels due to exposure to ABS vapors. Hence, corrective actions are required to protect the workers from non-cancer and cancer risks.