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Large lifting equipment is used regularly in the maintenance operations of chemical plant installations, where safety controls must be carried out to ensure the safety of lifting operations. This paper presents a convolutional neural network (CNN) methodology, based on the PyTorch framework, to identify unsafe behavior among lifting operation drivers, specifically, by collecting 22,352 images of equipment lifting operations over a certain time period in a chemical plant. The lifting drivers' behavior was divided into eight categories, and a ResNet50 network model was selected to identify the drivers' behavior in the pictures. The results show that the proposed ResNet50 network model based on transfer learning achieves a 99.6% accuracy rate, a 99% recall rate and a 99% F1 value for the expected behaviors of eight lifting operation drivers. This knowledge regarding unsafe behavior in the chemical industry provides a new perspective for preventing safety accidents caused by the dangerous behaviors of lifting operation drivers. [ABSTRACT FROM AUTHOR]

The chemical industry includes a wide range of factories focused on obtaining final products as: (i) plastics; (ii) chemical fibers; (iii) rubber; (iv) perfumery and cosmetic products; and (v) cleaning products. Although the level of safety in the activities and installations of this sector is very high, the use of dangerous substances implies an increased risk of suffering an accident involving the emission of hazardous substances, as well as endangering the safety of workers. In the case of the manufacture of softeners, the presence of isopropanol (C3H8O), and dimethyl sulfate (CH3)2SO4), have been reported to be the accident cause in most of the cases. The European accident database (eMars) reported an accident in which the presence of impurities of nickel (Ni) in the hydrogenated tallow used as raw material for softener production may have increased thermal reactivity and the chances of spontaneous combustion. This paper analyzes the results obtained with the Maciejasz Index (MI) to understand the thermal susceptibility of these substances in liquid state. The results show that combinations of nickel (hydrogenated tallow catalyst) with other liquid substances (isopropanol, dimethyl sulfate, and sulfuric acid) are not sufficiently reactive with oxygen to cause a spontaneous combustion. [ABSTRACT FROM AUTHOR]

A prediction method applicable to erosion-prone areas of fluorine chemical pipelines is herein proposed. By summarizing the common working conditions in the fluorine chemical industry, a computational fluid dynamics (CFD) model for industrial pipelines is established, and three-dimensional numerical calculations of the flow field in the pipelines are carried out to analyze the characteristics of the hydrodynamic parameters and phase distribution in the flow field, as well as the erosion rate distribution in different areas of the pipelines. The areas in the pipeline that are susceptible to erosion are predicted based on the results. The method has universal applicability and has been applied in many large petrochemical companies. It achieves the function of predicting the areas of severe pipeline erosion in a scientifically sound manner. This paper also details the application of the method in a distillation oil and gas pipeline of a large oil and gas company in western China. [ABSTRACT FROM AUTHOR]

Microwave heating has been widely used in the chemical industry because of its advantages, such as fast heating rate, selective and controllable heating, increasing reaction rate and reducing by-products in chemical reactions. The Special Issue contains research on microwave applications in chemical engineering. [ABSTRACT FROM AUTHOR]

The main aim of this study is to build the investor's profile in the Romanian chemical industry and to highlight the factors that influenced the decision of investing in Romania rather than other Central Eastern European countries. The data collection was performed in June 2019 and the list of the 150 foreign companies from the chemical industry was obtained from The National Trade Register Office. Data used in this research were collected using a questionnaire. Dependent variable represents the probability of investing in Romania, with the option of the other Central and Eastern European countries as reference group. The main part of our analysis focus on this question: "Which were the reasons that made you decide invest in Romania?" For analysis, a number of six main classes are used: Infrastructure, labor force, Agglomeration factors, Knowledge, Market Size and Cost factors (as independent variables). Main results consist in the presence of three factors with a positive impact. The paper also highlights that the main advantage considered by a foreign investor in Romania is represented by the cheap labor force. As a secondary conclusion, companies are also interested in other factors that are mentioned in the paper. [ABSTRACT FROM AUTHOR]

Methanol is an important raw material widely used in the chemical industry. This article addresses the challenge of fluctuations in green hydrogen as a feed stream for methanol production from renewable feedstock. For a staged reactor design, robust operating conditions are generated through the simultaneous steady-state optimization of 50 process scenarios. The feed can be split and fed separately to the different reactor stages. However, neglected transient effects may render this design infeasible under dynamic conditions concerning carbon conversion and reactor temperature constraints. To overcome this, an additional dynamic optimization is conducted to ensure a feasible operation by an optimized feed-forward control of feed distribution and shell temperatures. In practice, this is possible because the disturbance, i.e., fluctuation, is measurable and predictable in a short time frame. The optimization yields optimal operating conditions, resulting in a reactor that is dynamically feasible for measurable fluctuating inlet conditions. [ABSTRACT FROM AUTHOR]

Moreover, Clodoveo et al. [[10]] set an alternative and less drastic method based on ultrasound technology to produce carob syrup with respect to the traditional very time-consuming process, involving solid-liquid extraction in boiling water and concentration at a high temperature (>100 °C). Natural bioactive compounds include a plethora of structures and functionalities providing a consistent pool of molecules to produce nutraceuticals, functional foods, and food additives. Mass spectrometry (MALDI-TOF) analysis was employed to identify the EPA/DHA location at the sn-2 position in the resulting glycerol moiety. [Extracted from the article]

Propylene glycol, also known as 1,2 propanediol, is one of the most important chemicals in the industry. It is a water-soluble liquid, considered by the U.S. Food and Drug Administration as safe to manufacture consumer products, including foodstuffs, medicines, and cosmetics. This chemical has essential properties, such as solvent, moisturizer, or antifreeze, in addition to a low level of toxicity. This paper aims to present the selection, simulation, and dimensioning of a trickle bed reactor at a laboratory scale. The sizing was validated with other authors. Two predictive models have been considered for reactor modeling, intrinsic kinetics and coupled intrinsic kinetics, along with mass transfer equations and the wetting of the catalyst particles. The model was implemented using Aspen Custom Modeler® (20 Crosby Dr. Bedford, MA 01730, EE. UU.) to study the reactor behavior in terms of conversion. The results show the profiles of different variables throughout the reactor and present higher glycerol conversion when mass transfer is added to the model. [ABSTRACT FROM AUTHOR]

This paper describes the state of the art and future opportunities for process design and sustainable development. In the Introduction, the main global megatrends and the European Union's response to two of them, the European Green Deal, are presented. The organization of professionals in the field, their conferences, and their publications support the two topics. A brief analysis of the published documents in the two most popular databases shows that the environmental dimension predominates, followed by the economic one, while the social pillar of sustainable development is undervalued. The main design tools for sustainability are described. As an important practical case, the European chemical and process industries are analyzed, and their achievements in sustainable development are highlighted; in particular, their strategies are presented in more detail. The conclusions cover the most urgent future development areas of (i) process industries and carbon capture with utilization or storage; (ii) process analysis, simulation, synthesis, and optimization tools, and (iii) zero waste, circular economy, and resource efficiency. While these developments are essential, more profound changes will be needed in the coming decades, such as shifting away from growth with changes in habits, lifestyles, and business models. Lifelong education for sustainable development will play a very important role in the growth of democracy and happiness instead of consumerism and neoliberalism. [ABSTRACT FROM AUTHOR]

γ-valerolactone can be a game-changer in the chemical industry because it could substitute fossil feedstocks in different fields. Its production is from the hydrogenation of levulinic acid or alkyl levulinates and can present some risk of thermal runaway. To the best of our knowledge, no studies evaluate the thermal stability of this production in a continuous reactor. We simulated the thermal behavior of the hydrogenation of butyl levulinate over Ru/C in a continuous stirred-tank reactor and performed a sensitivity analysis. The kinetic and thermodynamic constants from Wang et al.'s articles were used. We found that the risk of thermal stability is low for this chemical system. [ABSTRACT FROM AUTHOR]

A chemical industry park (CIP) has a wide variety of hazardous chemicals, and once an accident occurs, the level of danger increases geometrically, while the domino effect may bring devastating consequences. To improve the emergency rescue capability of a chemical park and prevent the domino effect, a certain number of emergency centers are built at sites near the park for the purpose of rapid emergency rescue and deployment of emergency supplies. Based on this, in our study, a siting model of the emergency center of the chemical park, which aims to prevent the domino effect, was constructed by considering the timeliness and safety, while adopting the prevention of the domino effect as a constraint. The NSGA-II algorithm is used to solve the siting model, and the CPLEX method is used for the comparison. This study combines the prevention of the domino effect with multi-objective optimization theory, which has a good and simple applicability for solving the considered problem and can obtain solutions in line with science and reality. It also adds the risk radius of the demand point based on the traditional siting model and proposes a model that combines the risk and distance to reduce the risk of accidents across the whole region. Finally, the model is applied to a chemical park in China for an arithmetic analysis to provide decision makers with a targeted reference base for the siting of an emergency center. The experimental results show that the NSGA-II algorithm can effectively solve the model of the emergency center in the chemical park and outperforms the results obtained from the CPLEX solution in terms of its cost and safety. [ABSTRACT FROM AUTHOR]

Source term estimation (STE) is crucial for understanding and addressing hazardous gas leakages in the chemical industry. Most existing methods basically use an atmospheric transport and dispersion (ATD) model to predict the concentrations of hazardous gas leakages from different possible sources, compare the predicted results with multi-sensor data, and use the deviations to search and derive information on the real sources of leakages. Although performing well in principle, complicated computations and the associated computer time often make these methods difficult to apply in real time. Recently, many machine learning methods have also been proposed for the purpose of STE. The idea is to build offline a machine-learning-based STE model using data generated with a high-fidelity ATD model and then apply the machine learning model to multi-sensor data to perform STE in real time. The key to the success of a machine-learning-based STE is that the machine-learning-based STE model has to cover all possible scenarios of concern, which is often difficult in practice because of unpredictable environmental conditions and the inherent robust problems with many supervised machine learning methods. In order to address challenges with the existing STE methods, in the present study, a novel multi-sensor data-driven approach to STE of hazardous gas leakages is proposed. The basic idea is to establish a multi-sensor data-driven STE model from historical multi-sensor observations that cover the situations known as the independent hazardous-gas-leakage scenarios (IHGLSs) in a chemical industry park of concern. Then the established STE model is applied to online process multi-sensor data and perform STE for the chemical industry park in real time. The new approach is based on a rigorous analysis of the relationship between multi-sensor data and sources of hazardous gas leakages and derived using advanced data science, including unsupervised multi-sensor data clustering and analysis. As an example of demonstration, the proposed approach is applied to perform STE for hazardous gas-leakage scenarios wherein a Gaussian plume model can be used to describe the atmospheric transport and dispersion. Because of no need of ATD-model-based online optimization and supervised machine learning, the new approach can potentially overcome many problems with existing methods and enable STE to be literally applied in engineering practice. [ABSTRACT FROM AUTHOR]

As a consequence of increased awareness of environmental preservation and the associated rigorous regulations, the adoption of sustainable practices has become a crucial element for corporate organizations in regard to their supply chains. In the chemical industry, which is characterized by high risks, high pollution, and high efficiency, these characteristics can help businesses analyze their long-term development and sustainability. The goal of this research is to analyze and choose possible suppliers based on their sustainability performance in the chemical sector. A methodology based on multi-criteria decision making (MCDM) is proposed for this evaluation, using spherical fuzzy analytical hierarchy process (SF-AHP) and combined compromise solution (CoCoSo) methods, in which the novel spherical fuzzy sets theory is employed to present the ambiguous linguistic preferences of experts. In the first stage, an evaluation criteria system is identified through literature review and experts' opinions. The SF-AHP is used to determine the criteria weights, while the CoCoSo method is utilized to select the right sustainable supplier. A case study in the chemical industry in Vietnam is presented to demonstrate the effectiveness of the proposed approach. From the SF-AHP findings, "equipment system and technology capability", "flexibility and reliability", "logistics cost", "green materials and technologies", and "on-time delivery" were ranked as the five most important criteria. From the CoCoSo analysis, Vietnam National Chemical Group (CHE-05) was found to be the best supplier. A sensitivity study and a comparison analysis of methods were also conducted to verify the robustness of the proposed model, and the priority rankings of the best suppliers were very similar. To the best of our knowledge, this is the first study that has proposed SF-AHP and CoCoSo to prioritize SSS evaluation criteria and determine the best alternatives. The suggested method and findings can be used to make well-informed decisions that help businesses to achieve supply chain sustainability, capture opportunities, and maintain competitiveness through reconfiguring resources. The method could be useful for case studies in other countries and for other sustainability problems. [ABSTRACT FROM AUTHOR]

The increasing share of variable renewable energies in the power grid is an incentive to explore demand response strategies. Chlor-alkali processes are high potential candidates, according to previous publications. Within Germany's chemical industry, chlorine production accounts for approximately 20% of electricity use and could play a significant role in power grid stabilisation on the consumer end. This study focuses on the feasibility of load flexibilisation in epichlorohydrin plants, with the second biggest estimated demand response potential for chlorine-based products in Germany. A plant model with allyl chloride storage was created based on real data and literature values. Results from this model, spot market and balancing power prices, and future electricity market scenarios were used in a mixed-integer linear optimisation. We find that benefits from demand response can be generated as soon as additional power and storage volume is provided. The composition of provided types of balancing power bids follows the price trend on the market. Additionally, the computation time could be lowered significantly by running the scenarios in parallel. The results encourage a practical validation of the flexibility of epichlorohydrin production. [ABSTRACT FROM AUTHOR]

Traditional hazard and operability analysis (HAZOP) is one of the most widely applied methods for process safety management in process enterprises. Due to its principles based on the conservative and qualitative judgment, it often leads to too conservative risk identification results for the fluorine chemical industry usually with high-risk processes to keep the continuity of production. Most of improved quantitative and semi-quantitative methods are based on the layer of protection analysis (LOPA) to resolve the over-conservative problem of traditional HAZOP with the database of LOPA. However, the improved model, taking LOPA as the main line and HAZOP only as the provider of scenarios and influencing factors, is limited to the fact that LOPA can only analyze complete and independent protection layers (IPLs). Therefore, in order to realize the quantitative or semi-quantitative analysis of disaster causes and consequences, a new semi-quantitative HAZOP method takes HAZOP as the main line to integrate LOPA, F&EI (fire and explosion index) for quantitatively calculating the reduction factors, probability on failure demand (PFD) of general protection layers (GPLs) and PFD of IPLs. With the case comparison of fluorine chemical industry, it is proved that this new method can effectively improve the problem that traditional HAZOP are too conservative in complex scenarios. [ABSTRACT FROM AUTHOR]

When a major accident occurs in a chemical industry park, it directly affects the personal safety of operators and neighboring residents and causes major losses; therefore, we should take measures to strengthen the management of chemical industry parks. This article proposes and analyzes a new dynamic semi-quantitative risk calculation model for chemical plants that can be applied digitally. This model provides a sustainable, standardized, and comprehensive management strategy for the safety management of chemical plants and chemical industry park managers. The model and its determined parameters were applied to the safety management of chemical companies within the chemical industry park of Quzhou, Zhejiang Province. From the point of view of the existing semi-quantitative model, the existing problems of the current model are analyzed, the current model is optimized, and a new dynamic semi-quantitative calculation model scheme is proposed. The new model uses an analytical hierarchy process targeting the factors affecting the risks in chemical plants, and chemical plant semi-quantitative dynamic calculation system consisting of the operator, process/equipment, risk, building environment, safety management, and domino effect, and the comprehensive risk of the chemical plant was calculated. The model is ultimately a real-time quantitative value, but its calculation process can compare and analyze the causes of high risk in a chemical plant as they relate to these six factors. Its implementation requires only software, which will greatly help chemical plant safety management. [ABSTRACT FROM AUTHOR]

The literature has two different perspectives on which innovation types should be implemented to achieve innovation performance; some argue that they should pursue process-oriented innovation, while others maintain that both product- and process-oriented innovation should be performed. Though innovation efficiency should be measured, which takes both input and output variables into account, the research so far has been measured only with the performance of the innovation. Accordingly, this study identifies which innovation type is the most advantageous in terms of innovation efficiency for the chemical firms. We use data of 64 Korean chemical companies from a 2016 Korean innovation survey and perform data envelopment analysis to calculate innovation efficiency. Kruskal–Wallis one-way ANOVA and bootstrap DEA were also conducted to compare the difference of innovation efficiency among groups, depending on which innovation types are oriented. The result shows that focusing on process innovation rather than both product and process innovation is more appropriate to achieve higher innovation efficiency, and pursuing process innovation rather than product innovation tends to achieve a higher level of innovation efficiency in the Korean chemical industry. [ABSTRACT FROM AUTHOR]