Chemical Production Processes Research Articles

State Surveillance and Fault Diagnosis of Distillation Columns Using Residual Network-Based Passive Acoustic Monitoring

The operational state of distillation columns significantly impacts product quality and production efficiency. However, due to the complex operation and diverse influencing factors, ensuring the safety and efficient operation of the distillation columns becomes paramount. This research combines passive acoustic monitoring with artificial intelligence techniques, proposed a technology based on residual network (ResNet), which involves the transformation of the acoustic signals emitted by three distillation columns under different operating states. The acoustic signals were initially in one-dimensional waveform format and then converted into two-dimensional Mel-Frequency Cepstral Coefficients spectrogram database using fast Fourier transform. Ultimately, this database was employed to train a ResNet for the purpose of identifying the operational states of the distillation columns. Through this approach, the operational states of distillation columns were monitored. Various faults, including flooding, entrainment, dry-tray, etc., were diagnosed with an accuracy of 98.91%. Moreover, an intermediate transitional state between normal operation and fault was identified and accurately recognized by the proposed method. Under the transitional state, the acoustic signals achieved an accuracy of 97.85% on the ResNet, which enables early warnings before faults occur, enhancing the safety of chemical production processes. The approach presents a powerful tool for the monitoring and diagnosis of chemical equipment, particularly distillation columns, ensuring the safety and efficiency.

Comparative analysis of buoyancy-driven hydromagnetic flow and heat transfer in a partially heated square enclosure using Cu-Fe3O4 and MoS2-Fe3O4 nanofluids

Purpose This study aims to investigate entropy generation through natural convection and examine heat transfer properties within a partially heated and cooled enclosure influenced by an angled magnetic field. The enclosure, subjected to consistent heat production or absorption, contains a porous medium saturated with a hybrid nanofluid blend of Cu-Fe3O4 and MoS2-Fe3O4. Design/methodology/approach The temperature and velocity equations are converted to a dimensionless form using suitable non-dimensional quantities, adhering to the imposed constraints. To solve these transformed dimensionless equations, the finite-difference method, based on the MAC (Marker and Cell) technique, is used. Comprehensive numerical simulations address various control parameters, including nanoparticle volume fraction, Rayleigh number, heat source or sink, Darcy number, Hartmann number and slit position. The results are illustrated through streamlines, isotherms, average Nusselt numbers and entropy generation plots, offering a clear visualization of the impact of these parameters across different scenarios. Findings Results obtained show that the Cu-Fe3O4 hybrid nanofluid exhibits higher entropy generation than the MoS2-Fe3O4 hybrid nanofluid when comparing them at a Rayleigh number of 106 and a Darcy number of 10–1. The MoS2 hybrid nanofluid demonstrates a low permeability, as evidenced by an average Darcy number of 10–3, in comparison to the Cu hybrid nanofluid. The isothermal contours for a Rayleigh number of 104are positioned parallel to the vertical walls. Additionally, the quantity of each isotherm contour adjacent to the hot wall is being monitored. The Cu and MoS2 nanoparticles exhibit the highest average entropy generation at a Rayleigh number of 105 and a Darcy number of 10–1, respectively. When a uniform heat sink is present, the temperature gradient in the central part of the cavity decreases. In contrast, the absence of a heat source or sink leads to a more intense temperature distribution within the cavity. This differs significantly from the scenario where a uniform heat sink regulates the temperature. Originality/value The originality of this study is to examine the generation of entropy in natural convection within a partially heated and cooled enclosure that contains hybrid nanofluids. Partially heated corners are essential for optimizing heat transfer in a wide range of industrial applications. This enhancement is achieved by increasing the surface area, which improves convective heat transfer. These diverse applications encompass fields such as chemical engineering, mechanical engineering, surface research, energy production and heat recovery processes. Researchers have been working on improving the precision of heated and cold corners using various methods, such as numerical, experimental and analytical approaches. These efforts aim to enhance the broad utility of these corners further.

The role of industrial strategies in chemical production and service supply logistics processes

The research topic focuses on applying industrial strategies, especially in chemical industry logistics processes. The research aims to examine the future of the chemical industry in Hungary and worldwide. The question is what is the role of logistics in the chemical supply chain and the difficulties faced in this area? The main difficulty that can be encountered is achieving effective automation and digitalization while maintaining appropriate safety, which is an essential aspect of the chemical industry. The research presents an existing AGV transport solution that significantly facilitates on-site logistics processes within a chemical company. In the research, the potential development areas are found which could be interesting to explore in logistics processes are also discussed.

Enhanced efficiency and selectivity of carbon monoxide absorption by cuprous-based bimetallic deep eutectic solvents via iron activation

The absorption/utilization of carbon monoxide (CO) is critical due to its wide application in chemical production processes (e.g., acylation), and its toxicity/environmental impact. However, traditional copper-ammonia solutions in industry have limitations such as the deactivation of copper ions and high viscosity, which hinder efficient CO absorption under atmospheric conditions. In this work, a series of low-viscosity bimetallic deep eutectic solvents (DESs) with high CO absorption capacity were developed by mixing metal chlorides with [Emim]Cl and CuCl in specific molar ratios. Comprehensive studies of their physical properties (density and viscosity) and CO absorption performance revealed that [Emim]Cl-CuCl-1.0FeCl3 exhibited the lowest viscosity (57.4 cP) and a nearly equimolar CO absorption capacity (0.9 mol/mol). Moreover, the selectivity for CO/N2 (730), CO/CO2 (22), and CO/H2 (52) at 298.2 K and 100 kPa was also satisfied. The data of Raman and FT-IR spectra demonstrated that FeCl3 effectively activated cuprous sites in CuCl and significantly enhanced the CO absorption performance of cuprous-based bimetallic DESs. Furthermore, [Emim]Cl-CuCl-1.0FeCl3 showed excellent reusability after six absorption–desorption cycles. Therefore, we believe that this work provides a route for the effective activation of cuprous sites in cuprous-based bimetallic DESs to achieve efficient and reversible CO absorption at atmospheric conditions.

Performance of yellow and pink oyster mushroom dyes in dye sensitized solar cell

A solar photovoltaic (PV) cell, is an electrical device that uses the PV effect to convert light energy into electricity. The application of oyster mushroom dyes in dye sensitized solar cell (DSSC) is a novel strategy to substitute the costly chemical production process with easily extractable, environmentally acceptable dyes. Both dyes of yellow and pink oyster mushrooms were extracted using the same process but dried into powder form using two techniques, warm drying and freeze drying. The characterization was carried out utilizing current-voltage (I-V) characterization for electrical properties, Ultraviolet-Visible (UV-Vis) spectrophotometer for optical properties, Field Emission Scanning Electron Microscopy (FESEM), and Atomic Force Microscopy (AFM) for the structural properties. It was found that freeze-dried pink and yellow oyster mushroom had shown the good properties for DSSC application as it produced energy bandgap which lies within the range of efficient dye sensitizer; 1.7 eV and 2.2 eV, the most uniform distribution of pores and a nearly spherical form in FESEM analysis, and AFM result obtained with the highest root mean square (RMS) roughness value (26.922 and 34.033) with stereoscopic morphologies. The data proved that mushroom dyes can be incorporated in DSSC with the optimization of drying method in the extraction process, dilution of dye and the layer of deposition on the glass substrate. The current density-voltage (J–V) characteristics of fabricated DSSC was characterized using Newport Oriel Sol3A solar simulator under AM 1.5 Sun condition (100 mW/cm2, 25 oC). From the result obtained by solar simulator, the fabricated FTO/TiO2/Pleurotus djamor dye/Pt indicated the Voc of 0.499 V and Jsc of 0.397 mA/cm2.

Mechanistic investigations into the removal of Hg0 over Pt-based sorbents by warm syngas cleanup

Commercial sorbents for Hg0 removal from syngas operate at low temperatures (<120 ºC), which impacts the thermal efficiency of integrated gasification and chemical production processes. For the first time, supported Pt-based sorbents with different supports, viz., Pt/SBA-15, Pt/S-40, Pt/ZrO2, Pt/CeO2, and Pt/TiO2 are synthesized for high-temperature (≥200 ºC) Hg0 removal. Pt/SBA-15 shows the highest cumulative Hg0 removal efficiency (>99%) at 270 ºC in N2, which is retained for much higher durations (>20h) compared to the best-reported Pd-based and metal oxide sorbents. Besides amalgam formation, Hg0 removal on Pt/ZrO2 and Pt/CeO2 occurs via oxidation at high temperatures (> 270 ºC), which explains the reduced negative effect of high temperature over these sorbents. In contrast to other supports, the removal efficiency over Pt/SBA-15 is above 99% even in the presence of H2, H2O, and CO2, and is attributed to the hydrophobic mesoporous silica support. DRIFTS studies indicate CO adsorption on the active Pt sites over all sorbents. Based on the inferences obtained, a kinetic model is developed and integrated into a reactor-scale model which effectively simulates the spatio-temporal features of Hg0 sorption over Pt/SBA-15.

Reaction kinetics of Ni-Fe oxygen carriers with high performance for chemical looping hydrogen production

The chemical looping hydrogen production (CLHP) process utilizing iron-based oxygen carriers holds promise for practical hydrogen generation applications. However, challenges persist in developing iron-based carriers that are easy to prepare and demonstrate effective reactivity. Our research focuses on evaluating the CLHP reaction performance using bimetallic Ni-Fe oxygen carrier particles, synthesized by a scalable mechanical mixing method. This study unravels the role of Ni in bimetallic Ni-Fe oxygen carriers and investigates the influence of varying nickel-doping ratios on enhancing the reduction reactivity of iron-based oxygen carriers. Compared to the undoped sample, the 20 wt% nickel-doped Fe2O3/Al2O3 (20NiFe) particles enabled a reduction in the reaction temperature by at least 50 K. With the dopant, the methane conversion rate at 923 K is 500% higher than that of carriers without the dopant. Heterogeneous kinetic analysis demonstrates that the global reduction reaction data for the Ni-Fe oxygen carriers fit with the nucleation-nuclei growth model. Through redox reactivity tests and reduction kinetic analysis, 20NiFe shows the lowest apparent activation energy at 53.93 kJ/mol. Furthermore, the kinetic study and quasi in-situ XRD analysis aid in proposing the evolution process of methane reduction reaction for nickel-doped iron-based oxygen carriers, clarifying the role of doping in improving chemical looping reactivity from a kinetic perspective. This work provides valuable guidance for the design and optimization of oxygen carriers, thus accelerating the readiness for industrial deployment of hydrogen production via chemical looping process.

Identification of Yarrowia lipolytica as a platform for designed consortia that incorporate in situ nitrogen fixation to enable ammonia-free bioconversion

Bioconversion processes require nitrogen for growth and production of intracellular enzymes to produce biofuels and bioproducts. Typically, this is supplied as reduced nitrogen in the form of ammonia, which is produced offsite from N2 and H2 via the Haber-Bosch process. While this has revolutionized industries dependent on fixed nitrogen (e.g., modern agriculture), it is highly energy-intensive and its reliance on natural gas combustion results in substantial global CO2 emissions. Here we investigated the feasibility of in situ biological nitrogen fixation from N2 gas as a strategy to reduce greenhouse gas impacts of aerobic bioconversion processes. We developed an efficient and cost-effective method to screen fungal bioconversion hosts for compatibility with the free-living diazotrophic bacterium Azotobacter vinelandii under nitrogen fixing conditions. Our screening revealed that the genus Yarrowia is particularly enriched during co-culture experiments. Follow-up experiments identified four Y. lipolytica strains (NRRL Y-11853, NRRL Y-7208, NRRL Y-7317, and NRRL YB-618) capable of growth in co-culture with A. vinelandii. These strains utilize ammonium secreted during diazotrophic fixation of N2, which is provided as a component of the air input stream during aerobic fermentation. This demonstrates the feasibly of in situ biological nitrogen fixation to support heterotrophic fermentation processes for production of fuels and chemicals.

Assessment of safety: In vitro reverse mutation and in vivo acute oral toxicity tests of three biomass products from amino acid-producing Corynebacterium glutamicum

Microbial fermentation has emerged as a pivotal process for sustainable production of essential goods and chemicals. Corynebacterium glutamicum is a proficient platform organism that contributes significantly to amino acid production through microbial fermentation. Despite its recognized safety, challenges persist in efficiently biosynthesizing natural products compared with other organisms. This study evaluated the safety of biomass products from bioengineered C. glutamicum through two different toxicological studies: a bacterial reverse mutation test (AMES test) and an acute oral toxicity test in rats. Three types of dried fermentation biomass products, each engineered for the enhanced production of specific amino acids (L-lysine, L-threonine, and L-tryptophan), were examined. The tests were conducted in compliance with Organization for Economic Co-operation and Development guidelines and revealed no mutagenicity or acute toxicity at the tested doses. These findings suggest the safety of biomass products from bioengineered C. glutamicum as potential feed materials, although further toxicity studies are recommended for comprehensive evaluation. This study underscores the importance of stringent safety assessments for advancing biotechnological applications and provides valuable insights into the potential utilization of microbial fermentation products in various industries. Moreover, this study highlights the significance of regulatory compliance and adherence to international standards to ensure the safety and efficacy of novel biotechnological products.

Investigations of Metallurgical Differences in AISI 347 and Their Influence on Deformation and Transformation Behaviour and Resulting Fatigue Life.

Due to variations in chemical composition and production processes, homonymous austenitic stainless steels can differ significantly regarding their initial microstructure, metastability, and thus, their fatigue behavior. Microstructural investigations and fatigue tests have been performed in order to evaluate this aspect. Three different batches and production forms of nominally one type of steel AISI 347 were investigated under monotonic tensile tests and cyclic loading under total strain and stress control in low and high cycle fatigue regimes, respectively. The deformation induced α'-martensite formation was investigated globally by means of in situ magnetic measurements and locally using optical light microscopy of color etching of micrographs. The investigation showed that the chemical composition and the different production processes influence the material behavior. In fatigue tests, a higher metastability and thus a higher level of deformation induced α'-martensite pronounced cyclic hardening, resulting in significantly greater endurable stresses in total strain-controlled tests and an increase in fatigue life in stress-controlled tests. For applications of non-destructive-testing, detailed knowledge of a component's metastability is required. In less metastable batches and for lower stress levels, α'-martensite primarily formed at the plasticization zone of a crack. Furthermore, the formation and nucleation points of α'-martensite were highly dependent on grain size and the presence of δ-ferrite. This study provides valuable insights into the different material behavior of three different batches with the same designation, i.e., AISI 347, due to different manufacturing processes and differences in the chemical composition, metastability, and microstructure.

Three‐layer deep learning network random trees for fault detection in chemical production process

AbstractWith the development of technology, the chemical production process is becoming increasingly complex and large‐scale, making fault detection particularly important. However, current detection methods struggle to address the complexities of large‐scale production processes. In this paper, we integrate the strengths of deep learning and machine learning technologies, combining the advantages of bidirectional long‐ and short‐term memory neural networks, fully connected neural networks, and the extra trees algorithm to propose a novel fault detection model named three‐layer deep learning network random trees (TDLN‐trees). First, the deep learning component extracts temporal features from industrial data, combining and transforming them into a higher‐level data representation. Second, the machine learning component processes and classifies the features extracted in the first step. An experimental analysis based on the Tennessee Eastman process verifies the superiority of the proposed method.

F-Norm-Based Soft LDA Algorithm for Fault Detection in Chemical Production Processes.

In chemical production processes, outliers are inevitable. Many existing feature extraction algorithms are overly sensitive to outliers and excessively focus on secondary features while ignoring the key features in the data. To address this problem, the Frobenius norm based soft linear discriminant analysis algorithm (FBSLA) is proposed in this paper. Specifically, FBSLA uses the Frobenius norm instead of its square as a metric to enhance the robustness of the algorithm. Furthermore, a nonreduced dimensionality projection matrix is introduced to make the training data features more obvious. Additionally, soft constraint is adopted instead of the traditional hard constraint to reduce the sensitivity caused by outliers. To validate the effectiveness of FBSLA, in this paper, experiments are conducted on the Tennessee Eastman Process and the Penicillin Fermentation Process data sets. According to experimental results, FBSLA significantly outperforms other state-of-the-art algorithms in terms of fault detection accuracy.

Life cycle assesment on water treatment: Case study in Cau Do water treatment plant – Da Nang city - VietNam

Abstract Production, transportation, use, and disposal consumes resources, energy, and chemicals and causes emissions and waste, contributing to various impacts on the environment, such as heat, global warming, and acidification. Applying the Life Cycle Assessment (LCA) method can identify environmental impacts, to have solutions to control energy use and reduce waste. Research applied at Cau Do water supply plant - Da Nang city with an exploitation flow of 290,000 m3/day shows the results that the plant’s water treatment system has impacts within the allowable limits for with the geological environment in the area, but exploitation requires strict assessment to prevent possible unusual impacts. Assess the environmental impact of using electricity and chemicals in the system by estimating CO2 emissions from the electricity and chemical production process, which indirectly contribute to global warming. Direct sources of greenhouse gases come from the treatment of sludge and wastewater. The impacts discussed in this study are Global Warming, Acidification, depletion of fossil resources and Water Consumption. The amount of electricity consumed at Cau Do plant in 2018 was 16,742,752 kWh/year. The results of the analysis using the Life Cycle Assessment (LCA) method, show that the largest impact on the environment is Water Consumption 1.101 m3/m3 clean water, followed by Global Warming 0.158 kg CO2 eq/m3 Acidification 1.06 × 10−3 kg SO2 eq/m3.

Real-time risk prediction of chemical processes based on attention-based Bi-LSTM

Refined risk prediction must be achieved to guarantee the safe and steady operation of chemical production processes. However, there is high nonlinearity and association coupling among massive, complicated multisource process data, resulting in a low accuracy of existing prediction technology. For that reason, a real-time risk prediction method for chemical processes based on the attention-based bidirectional long short-term memory (Attention-based Bi-LSTM) is proposed in this study. First, multisource process data, such as temperature, pressure, flow rate, and liquid level, are preprocessed for denoising. Data correlation is analyzed in time windows by setting time windows and moving step lengths to explore correlations, thus establishing a complex network model oriented to the chemical production process. Second, network structure entropy is introduced to reduce the dimensions of the multisource process data. Moreover, a 1D relative risk sequence is acquired by max–min deviation standardization to judge whether the chemical process is in a steady state. Finally, an Attention-based Bi-LSTM algorithm is established by integrating the attention mechanism and the Bi-LSTM network to fit and train 1D relative risk sequences. In that way, the proposed algorithm achieves real-time prediction and intelligent perception of risk states during chemical production. A case study based on the Tennessee Eastman process (TEP) is conducted. The validity and reasonability of the proposed method are verified by analyzing distribution laws of relative risks under normal and fault conditions. Also, the proposed algorithm importantly improves the prediction accuracy of chemical process risks relative to that of existing prediction technologies.

Waste solution of acrylic-based emulsion with low solid content for cement concrete curing: a lab-scale study

In this study, we investigate using acrylic and acrylic-styrene emulsions as curing solution to enhance water resistance and prevent cracking in cement concrete surfaces. Our main objective is to investigate the possibilities of utilizing washing waste generated during the Rhomn &amp; Haas factory - Dow Chemical Vietnam production process. This waste is currently being treated as solid even though it contains anywhere from 10-45% solid content. Through laboratory-scale experiments and analysis, we aim to assess the feasibility of employing this waste material as a cost-effective, technically sound, and environmentally sustainable solution for curing compound of concrete. The findings of this research have the potential to contribute to the advancement of eco-friendly waste management techniques while improving the durability and effectiveness of cement structures.

Soft sensor modeling based on masked convolutional transformer block deep residual shrinkage network

BackgroundData-driven soft sensor technology is currently an important means for industrial data prediction, addressing the challenge of predicting key quality variables in industrial production processes. Convolutional neural networks (CNN) are widely applied in soft sensor modeling due to their excellent nonlinear modeling and feature extraction capabilities. However, CNN also faces several issues, such as poor robustness to interference and difficulties in extracting features from complex process data. MethodsThis paper introduces a novel CNN model called the Masked Convolutional Transformer Block Deep Residual Shrinkage Network (MCTB-DRSN). Firstly, the Masked Convolutional Transformer Block (MCTB) is utilized to extract features from different positions, thereby enabling the network model to focus more on important information. Secondly, the Global Response Normalization (GRN) layer is incorporated into the Deep Residual Shrinkage Network (DRSN) module, which enhances feature competition among channels. Significant FindingsThis method can provide effective monitoring for chemical production process. Compared with the traditional soft sensor method on the debutanizer column dataset, the results show that the prediction accuracy of this model is significantly improved.

(La1−xCax)MnO3−δ (x = 0, 0.2, 0.3, 0.4) Perovskites as Redox Catalysts in Chemical Looping Hydrogen Production Process: The Relation between Defect Chemistry and Redox Performance

The interaction between point defects in (La1−xCax)MnO3−δ (x = 0, 0.2, 0.3, 0.4) perovskites and their redox catalytic properties in a three-reactor chemical looping hydrogen production process is investigated. During the reduction step with CH4, the behavior of the materials is extrinsically determined and strongly depends on the Ca content. At small oxygen deficiencies, CH4 becomes oxidized to CO2. As the deficiency increases, partial oxidation to CO and H2 at a molar ratio of approximately 2 is favored. During the water-splitting step, the dependency on the Ca content is much weaker since it is intrinsically determined by the Mn2+→Mn3+ oxidation with simultaneous annihilation of oxygen vacancies that are not required to compensate for the extra negative charge of the Ca dopant. Hydrogen productivities in the order of 13 cm3 (STP) H2/g solid could be achieved during the water-splitting step at 1000 °C. The materials exhibited reproducible catalytic behavior during 10 cycles of the complete three-step process and were found to retain their perovskite structure.

Theoretical study of the mechanism of hydrogen production by catalytic methane decomposition on the carbon black catalyst

Catalytic methane decomposition (CMD) is a promising chemical process for hydrogen production from natural gas with zero CO2 emissions. In the present work we perform a systematic study of CMD on the graphene edge as a model of the carbon catalyst surface. Atomistic first-principles calculations (DFT level of theory) were performed to obtain the parameters of elementary reactions including the formation of active sites, interaction of methane molecules with these active sites and regeneration of the carbon catalyst surface. Our findings show that methane pyrolysis on the carbon catalyst is a unique heterogeneous radical chain process in which active sites are migrating over the catalyst surface via gas phase radical transport. Based on quantum-chemistry calculations, detailed kinetic mechanism of CMD on the carbon catalyst was developed and verified with respect to the latest experimental data. Calculated effective activation energies and methane conversions in CMD process are in reasonable agreement with experimental data. The present investigation of the mechanism of hydrogen production by CMD process on carbon catalyst can be useful for optimization of this process taking into account degradation of the carbon catalyst.

Modeling and simulation of a novel chemical process for clean hydrogen and power generation

The present work aims to develop a novel chemical process for clean hydrogen and power production and simulate it accordingly through a unique thermodynamic equilibrium model. This particular process is based on a partial oxidation of hydrogen sulfide (H2S) at superadiabatic conditions to study its respective chemical products. The simulation of superadiabatic partial oxidation of H2S is developed through the present model for the first time in the Aspen Plus. The process is further studied by varying different operating variables with an overall goal of optimizing the H2S conversion into hydrogen. The developed model predicts a satisfactory H2 production flow rate coupled with a low-sulfur dioxide (SO2) output within the superadiabatic partial oxidation regime at an operating pressure below 0.5 bar. The H2S conversion into H2 is then found to be 23.48 % at 0.25 bar. The overall energy and exergy efficiencies of the system are found to be 87.51 % and 70.08 % respectively. The dissociation of H2S in the presence of stoichiometric air results in elemental sulfur and hydrogen production rates of 0.0019 kg/s and 0.0012 kg/s, respectively.

A Mini-Review on Safe Treatment and Valorization of Salt Waste in Chemical Production Processes in China

In chemical production processes in China, a huge volume of salt waste is produced, contributing to environmental pollution due to the lack of proper treatment procedures to manage this waste effectively. With the current lack of proper treatment methods for salt waste disposal, landfill emerges as the predominant disposal approach adopted in China, exacerbating environmental concerns associated with the accumulation of such waste. In addition, this method fails to reuse and recycle salt waste. This review paper presents pertinent research on the safe disposal and utilization of salt waste in China. Well-known valorization methods, such as oxidation, thermal treatment, washing separation, precipitation, and evaporation crystallization, are comprehensively reviewed. The current technologies for recovering resources from salt waste and recommendations for its treatment and valorization are analyzed. This research work offers a reference for future resource utilization of industrial salt waste.