CO2 Concentration In Gas Research Articles

Neutralization and CO2 fixation behavior of alkaline recycled soil using column tests with CO2 ventilation

AbstractColumn tests were conducted to elucidate the pH neutralization and CO2 fixation behavior of alkaline recycled soils permeated with CO2. The ventilation period required for complete CO2 fixation (tEOF) was estimated from the test results of each column. The maximum amount of CO2 captured per gram of dry soil ((mCO2)max) was determined based on the inflow and outflow of CO2 for each specimen. The investigation into the effects of soil density, specimen height, CO2 gas concentration, and flow rate on tEOF and (mCO2)max revealed that tEOF increased with higher dry density (ρd) or specimen height (H), as a denser and larger specimen consumed more CO2 gas. Conversely, tEOF decreased with a higher CO2 gas concentration (C) or flow volume (Q) because a low C or Q resulted in an insufficient CO2 gas supply to match the consumption induced by the reaction. Lower C and Q tended to yield higher (mCO2)max, whereas higher ρd and H resulted in higher (mCO2)max. Furthermore, (mCO2)max increased with tEOF, with a slight reduction in pH after neutralization with increased tEOF. This phenomenon was attributed to the longer ventilation period enabling more Ca to react with the CO2 gas and maintain the equilibrium state of the reaction. Finally, the evaluation of the CO2 consumption rate (CSR) for each column specimen revealed that a higher C and Q resulted in a lower CSR, whereas a higher H resulted in a higher CSR. Additionally, a longer tEOF was associated with a higher CSR, although some variation was observed.

Synthesis of titanium-vanadium oxide thin films through thermal oxidation process for their use in CO gas sensing application

The thermal oxidation of evaporated titanium-vanadium thin films was carried out at different post-annealing temperatures. The structural, morphological, compositional, optical, electrical, and gas sensing properties of titanium-vanadium oxide thin films were primarily investigated. The results of the XRD study and Raman spectroscopy verified the presence of the orthorhombic V2O5, tetragonal TiO2, and monoclinic V2Ti3O9 phases. The crystallinity of TVO thin films was improved at higher annealing temperatures. The grain size (seen from SEM images) and oxygen compositions (obtained from EDS measurement) of TVO samples are also enhanced when the post-annealing temperature is increased from 500 to 575 °C. From the transmittance curves, the bandgap values for TVO samples were estimated and found in the range of 2.14–2.36 eV. The n-type conductivity of TVO films was confirmed by the negative Hall coefficient values. At last, the CO gas sensing response of TVO thin films was analyzed by monitoring the change in the surface electrical resistances at different operating temperatures and CO gas concentrations. The dynamic and static resistances were assessed at different operating temperatures varying from 100 to 300 °C. The TVO sample prepared at 550 °C showed the best conditions by examining the materials and CO sensing properties.

Gas sensitivity evaluation of decomposition gases in environmentally friendly insulating devices (C4F7N/CO2) (Chromium cluster-modified BNNTs surface interface at the atomic scale)

Under the global trend of green development, the research and development of environmentally friendly gas-insulated electrical equipment and the associated technical issues in the power systems industry have become important research topics. This study investigates the monitoring technology for decomposition gases (CO, C2F6, and COF2) under insulation defect conditions in environmentally friendly GIS equipment based on the green insulating gas mixture C4F7N/CO2. Initially, chromium clusters (Crn, n = 1–4) were used to modify one-dimensional boron nitride nanotubes (BNNTs), describing the changes in semiconductor electronic properties and magnetism at the microscopic interface. Interestingly, the electronic properties of the system changed significantly after modification, with increased electron mobility and enhanced conductivity. Subsequently, adsorption tests were conducted on three potential target gases, revealing that Cr-BNNT exhibited excellent adsorption for COF2, with an adsorption energy reaching -9.555 eV. This indicates its potential as a clean material for addressing COF2 (toxic) and CO leaks. Lastly, and most importantly, in the assessment of sensing performance, it has been discovered that Cr-BNNT holds promise as a gas sensor for C2F6. Furthermore, to meet diverse sensing requirements and environmental conditions, Cr3-BNNT and Cr4-BNNT have demonstrated potential as sensor array materials for the detection of CO (300 K, 0.145 μs) and COF2 (500 K, 3 min) gas concentrations, respectively. Our study contributes to the advancement of novel eco-friendly insulating gases and explores the use of new materials for monitoring the insulation performance and condition assessment of environmentally friendly GIS equipment.

Suppression of cross-interference in the absorption spectra of gas mixtures

In the quantitative analysis of mixed gases by tunable diode laser absorption spectroscopy, the overlapping of absorption spectra and mutual interference of multi-component gases can lead to problems of large measurement errors and low analysis accuracy. In this paper, an improved firefly algorithm is proposed and applied to the support vector machine regression model to solve this problem. The specific method includes introducing an adaptive step size to balance the local and global searches and using the gradient descent method to accelerate the parameter optimization process so as to improve the model’s generalization ability and prediction accuracy. The experimental results show that the maximum errors of the improved algorithm in the prediction of CH4 and CO gas concentrations are no more than 0.0443 % and 2 ppm, with coefficients of determination, R2, of 0.9994 and 0.99815. The promising results obtained by the system provide theoretical support for the realization of high-precision detection of multicomponent gases with a single source of light, and also demonstrate the high efficiency and feasibility of the method in practical detection.

High Performance‐Low Cost‐Chemiresitive BaTiO3 Nanospheres Based CO2 Gas Sensor for Air Quality Monitoring

AbstractSpherical shaped Barium titanate (BaTiO3) nanospheres was synthesized, using co‐precipitate technique. The as‐synthesized nanospheres was discovered to be discriminatory in terms of sensing carbon dioxide (CO2) gas. The structural parameters like, shape, optical properties, morphology, and thermal stability were explored using XRD, FT‐IR, UV‐Visible, EDS and FE‐SEM. Spherical shaped BaTiO3 nanospheres material have the highest response (63 %) for CO2 gas. CO2 gas reaction and recovery times were 23.5 and 20.3 seconds, respectively. The CO2 gas concentration climbs from 1 to 50 ppm, reaction to gas sensors increases. CO2 gas has higher selectivity for BaTiO3 nanospheres as compared to other gases like carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), nitric oxide (NO), hydrogen sulphide (H2S) and hydrogen (H2). The repeatability of BaTiO3 nanospheres was researched for 42 days and discovered to be impressive.

Identification and Ranking of Factors Affecting the Emission of CO Gas in Karkar Coal Mine

Karkar coal mine is one of the biggest mines in the country, where mining has been going on since 1938. During the mining period, the Karkar mine has witnessed unfortunate incidents caused by the release and explosion of gases, which have caused substantial financial and human losses. Carbon monoxide gas is one of the essential factors in the occurrence of accidents in the Karkar mine. This research includes literature reviews, field data collection using the CEM CO-181 model gas meter, and statistical calculations using Shannon entropy and Promethee methods. First, the concentration of CO gas was measured in ventilation tunnels, development, and excavation areas. Extraction workshops of the Karkar coal mine at different working times, and then 26 cases that may result in a reduction in accidents were used as effective criteria, and 9 cases were used as influential factors on CO emissions through the order and distribution of questionnaires and interviews with identified experts. The final weights of twenty-six effective criteria on the emission of CO gas were calculated based on the Shannon entropy method. As a result, the criterion of consumed oils with a final weight of 0.1790 was ranked first, and the criterion of lack of experience with a final weight of 0.1065 was ranked last. The influential factors have been ranked based on the amount of net flow and the parameter method. As a result, the factors of mining fire and coal dust explosion ranked first and last, respectively, with net flows of 0.55 and -0.84, and the rest of the factors are placed in different positions according to their net flow rate and have their effects on the emission of CO gas.

The Effect of CO on the Exothermic Characteristics of Low-Temperature Oxidation in Coal

ABSTRACT In recent years, abnormally high concentrations of CO gas have often been detected in goaf areas of coal mines in China, which have posed a serious threat to safe production in coal mines and the life and safety of underground personnel. The effect of CO on the exothermic characteristics of low-temperature oxidation in coal was studied by analyzing the exothermic characteristics, heat release, oxidation stages, and activation energy of coal at different CO concentrations using a C600 microcalorimeter. The experimental results indicated that CO promoted the coal oxidation at low temperature, but inhibited it after reaching 144°C. When the CO concentration is 30%, the exothermic characteristics of coal in low-temperature oxidation adsorption stage were significantly affected, with the heat flow value decreased by 27.0% and the initial exothermic temperature point moved forward by 3.9 K. In the later stage of the oxidation reaction, the heat release of coal was linearly negatively correlated with CO concentration. The higher the concentration, the lower the heat release. These findings contribute to understanding the combustion mechanism of coal in high-concentration CO atmosphere and provide valuable insights for designing and optimizing coal utilization technologies.

Investigating the Relationship between Urbanization and Air Pollution Using Google Earth Engine Platform: A Case Study of Istanbul

Rapid population growth in megacities such as Istanbul has led to various effects, such as industrialization, urbanization, loss of green areas, increasing vehicle traffic, and higher consumption of fossil fuels. These reasons, along with many other environmental factors, contribute to the rise of air pollution in urban life. This study aimed to examine the relationship between urbanization and air pollution in Istanbul. For this purpose, land cover maps covering Istanbul province were produced using Landsat-5 (TM), Landsat-8 (OLI), and Sentinel-2 (MSI) images for the years 1996 to 2021 at three-year intervals on the Google Earth Engine platform. Land cover for classification purposes was divided into five different classes: forest, water surface, urban area, and bare land, and classified using a random forest machine learning algorithm. To examine the impact of this urban area growth on air pollution, in the second step of the study, the column number density values of Sentinel 5P (TROPOMI) data for SO2, NO2, CO, and O3 gases for 2019, 2020, and 2021 were analyzed. The averages of the data from 39 air pollution monitoring stations across Istanbul were also examined. According to this classification, the urban area expanded from 491 km2 in 1996 to 1222 km2 by 2021. Considering the total surface area of Istanbul province, the urban area, which was 9% in 1996, reached 23% by 2021. The TROPOMI values were calculated as follows: the average column number density values for SO2, NO2, CO, and O3 were 0.0003538 mol/m², 0.0339514 mol/m², 0.0000984 mol/m², and 0.1453515 mol/m², respectively. Similarly, the gas concentrations of SO2, NO2, CO, and O3 measured from the ground stations were calculated as 6.603 µ/m3, 786,815 µ/m3, 43.763 µ/m3 and 45.773 µ/m3, respectively. Correlations between urbanization and TROPOMI values revealed a positive correlation of 0.39, 0.02, and 0.80 for SO2, NO2, and CO gases, while a negative correlation of 0.25 was found for O3 gas. The study also examined correlations between TROPOMI and ground station measurements, resulting in positive correlations of 0.55, 0.66, and 0.16 for SO2, NO2, and CO gases, respectively, while a negative correlation of 0.05 was found for O3 gas. Based on these findings, among the air pollutants studied both through TROPOMI and ground station data, the highest correlation was observed for CO gas.

Efficient multi-objective optimization and operational analysis of amine scrubbing CO2 capture process with artificial neural network

Amine scrubbing processes for post-combustion CO2 capture have been extensively studied and significantly improved via various novel designs. However, the amine scrubbers implemented nowadays were usually not optimized according to a number of different evaluation criteria. This is often due to the fact that, for high dimensional design spaces, the rigorous simulation runs needed to facilitate process optimization always calls for huge numbers of simulation software accesses and overwhelming iterative calculations. Therefore, in this study, the well-trained surrogate model was adopted to replace its rigorous counterpart for the purpose of ensuring efficient optimization runs in practical applications. In current study, two objectives, i.e., the specific equivalent work and the CO2 capture level, were both rapidly and effectively optimized in various practical scenarios with different flue gas CO2 concentrations. The corresponding operational parameters and utility consumptions were also easily obtained without additional effort. The computation results obtained so far showed that the proposed surrogate-assisted approach can be utilized to significantly reduce the computational load in practice.

Gas production characteristics of oats and tritical silages and techniques for reducing gas emissions1

Greenhouse gas (GHG) production during ensiling not only causes the nutrient losses of silage but also promotes climate warming. However, there is little information on the production of GHG and strategies for mitigating GHG emissions during ensiling. This work aimed to study the gas production characteristics and techniques for reducing gas emissions during ensiling. Oats and triticale, with Lactiplantibacillus plantarum (LP) or corn meal (CM) addition, were ensiled. The cumulative gas volume rapidly increased and reached to the peak within the first 9 days of ensiling for both forage crops. The highest cumulative gas volume of triticale silage was twice as much as that of oats silage. Triticale silage produced lower carbon dioxide (CO2) concentration, higher methane (CH4) and nitrous oxide (N2O) concentrations than oats silage within the 28 days of ensiling. Adding LP or CM significantly improved the fermentation quality and decreased the gas volume and GHG concentrations of two silages on d56 (except CH4 of triticale). At the early stage of ensiling, more Enterobacter, Lactococcus and Leuconostoc related to gas production were observed, and adding LP increased the abundance of Lactobacillus and decreased the abundance of bacteria like Kosakonia, Pantoea, Enterobacter and Lactococcus positively correlated with gas volume, CO2 and N2O concentrations. These results suggest that gas formation during ensiling mainly occurs in the first 9 days. Adding LP or CM can significantly improve the fermentation quality and decrease the gas volume. This would benefit to reducing GHG emissions in silage production.

Effect of Different Concentration of CO2 Gas Injected into Fresh Cement Paste Along with the Addition of Superplasticizer on the Mechanical Properties of Cement

This paper focused on examining the impact of injecting varying concentrations of carbon dioxide (CO2) gas on the mechanical characteristics of both the fresh and hardened states of cement paste. This study also considered the influence of the presence or absence of polycarboxylate superplasticizer in cement mixture on these properties. Many researchers discovered the benefit of CO2 gas utilization in cement mixture to accelerate the early strength of cement or concrete hydration by its carbonation that form calcium carbonate (CaCO3). The application method is to directly inject CO2 gas in a curing chamber for air-curing of precast concrete. Alternatively, carbonated water is mixed with cement during concrete mixing. However, the use of CO2 gas does not significantly improve the 28-day strength of concrete. This study explores how to improve the carbonation impact on mechanical properties of cement paste and apply it to ground improvement. In this study, the method adopted is direct injection of CO2 gas during cement slurry mixing with different injection duration. The influence of CO2 in the presence of superplasticizer (SP) in cement slurry was also studied as SP is generally used for grouting. The results showed that the carbonation of cement paste with additional of superplasticizer significantly affect its flow, viscosity and bleeding properties. Unlike samples with SP addition, the samples without SP addition showed higher compressive strength after 28 days of curing up to certain CO2 injection time. For all CO2 gas injection time, smaller porosity rates were observed for 7-day cured samples with SP addition compared to those without SP addition. This is due to accelerated carbonation due to SP presence in cement mixture. From the results, the optimum of CO2 gas injection time for one liter mixture of cement paste to improve its compressive strength (up to 123% increase) have been discovered. It can be inferred that the addition of superplasticizer in cement slurry reduces the amount of CO32- ions and Ca2+ ions during carbonation process of cement hydration products, which are strongly related to the pH level in pore solution. These ions play a significant roles in determining the mechanical properties of cement slurry.

Determination of Gas–Oil minimum miscibility pressure for impure CO2 through optimized machine learning models

Minimum miscibility pressure (MMP) is one of the most important parameters for designing CO2 enhanced oil recovery (EOR) and associated storage in depleted oil reservoirs. The injection gas stream often contains a certain concentration of impurities such as N2, H2S, and CH4 depending on the source of CO2. These impurities have different effects on CO2 MMP, but there is a lack of widely accepted approaches to account for these effects on MMP calculation. In this study, a series of activities were conducted to develop a machine learning (ML)-based methodology for determining MMP for CO2 with various impurities. A database containing 234 CO2 MMP test sets with around 5000 data points was built based on the reported experimental measurements in the public domain. The database was then subgrouped by three specific criteria: CO2 concentration in the injection gas, type of impurities in the injection gas, and heavier hydrocarbon content in the oil. This subgrouping was essential to capture the impact of different factors on CO2 MMP. An ensemble ML approach with seven ML models, including random forest, adaptive boosting, light gradient boosting machine, extreme gradient boosting (XGBoost), stacking, artificial neural network, and voting regressor, was employed to calculate MMP based on the subgrouped database. The hyperparameters of these ML models were optimized by the grid search technique to minimize the relative errors between calculated and measured MMP values. The performance of each algorithm was assessed using three regression metrics: average absolute relative error (AARE), R-squared score (R2), and root mean square error (RMSE). All of these metrics exhibited satisfactory values for the optimized ML models. The average values of R2, RMSE, and AARE were 0.962, 1.571, and 4.55%, respectively, for the three subgroups, indicating a high accuracy of MMP calculations using the optimized ML models. The XGBoost model emerged as the top performer across the three metrics, with an R2 of 0.979, an AARE of 2.835%, and an RMSE of 1.183 for a dataset with 190 cases. The overall high level of accuracy confirmed the reliability of these ML models in calculating MMP for CO2 with different impurities as well as the importance of optimization in the modeling process.

Corrosion Characteristics of Polymer-Modified Oil Well Cement-Based Composite Materials in Geological Environment Containing Carbon Dioxide.

Oil well cement is easily damaged by carbon dioxide (CO2) corrosion, and the corrosion of oil well cement is affected by many factors in complex environments. The anti-corrosion performance of oil well cement can be improved by polymer materials. In order to explore the influence of different corrosion factors on the corrosion depth of polymer-modified oil well cement, the influence of different corrosion factors on corrosion depth was studied based on the Box-Behnken experimental design. The interaction of different influencing factors and the influence of multiple corrosion depths were analyzed based on the response surface method, and a response surface model was obtained for each factor and corrosion depth. The results indicate that within the scope of the study, the corrosion depth of polymer-modified oil well cement was most affected by time. The effects of temperature and the pressure of CO2 decreased sequentially. The response surface model had good significance, with a determination coefficient of 0.9907. The corrosion depth was most affected by the interaction between corrosion time and the pressure of CO2, while the corrosion depth was less affected by the interaction between corrosion temperature and corrosion time. Improving the CO2 intrusion resistance of cement slurry in an environment with a high concentration of CO2 gas can effectively ensure the long-term structural integrity of cement.

Optimizing biofuel production from algae using four-element framework: Insights for maximum economic returns

This paper introduces a Four Element Strategy (FES) focused on concentration of CO2 gas (Cc,g), energy input to the process, concentration of nutrients, and the type of algal strain to maximize biofuel production (BYP) from algae strains and evaluate associated economic benefits. Two algal strains encompassing slain algae Nannochloropsis sp. (Ns), and freshwater strain Chlorella vulgaris (Cv) were used in the current study. Response Surface Methodology (RSM) and Box Behnkin Design (BBD) are integrated with a biokinetic model to explore the relationships among growth parameters in the ranges; Cc,g ∼ 0.03–20 %), Nitrogen (N) to phosphorus (P) ratio (RN/P)∼1–11, and light intensity (LI)∼ 100–400 µE m⁻² s⁻¹ with corresponding CO2 bio-fixation rate (RCO2, g L⁻¹ d⁻¹), biomass accumulation (X, g L⁻¹), and algal growth rate (µ, d⁻¹). Optimization results indicate that the most productive conditions involve LI in the range of 100–270 µE m⁻² s⁻¹, Cc,g between 0.03 % and 15 %, and RN/P of 3.5–11, achieving maximum RCO2, X, and µ at 1 g L⁻¹ d⁻¹, 6 g L⁻¹, and 1.6 d⁻¹, respectively. Further optimization using the desirability function supports achieving RCO2 of 1.2 g L⁻¹ d⁻¹, X of 1.8 g L⁻¹, and µ of 1.7 d⁻¹, with RN/P, Cc,g, and LI at 11, 4.5 %, and 200 µE m⁻² s⁻¹, respectively. Notably, Ns demonstrated higher nutrient uptake rates and BYP compared to Cv, with total production costs of $0.629/L and respective required cost coverages of $0.138 and $0.375 for Ns and Cv. This study offers a comprehensive guideline for large-scale microalgae cultivation technology (MCT) processes, providing insights into optimizing conditions for biofuel production while considering economic viability.

Post-modified porous aromatic frameworks for carbon dioxide capture

A new method was developed for post-modification of porous aromatic framework-1 (PAF-1) with chloride and then amine groups confirmed by different characterizations such as nuclear magnetic resonance (NMR), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FTIR). Compared to PAF-1, the amine-functionalized PAF-1 (PAF-1-NH2) exhibits a 50% improvement in carbon dioxide (CO2) adsorption capacity reaching 42 cm3·g-1 at room temperature and 1 bar, and the uptakes under CO2 concentration in air (4 kPa) and flue gas (150 kPa) also greatly increase. The column breakthrough experiments showed that PAF-1-NH2 can separate CO2 from a simulated flue gas of CO2/N2 (15:85, v/v) indicating its potential applications in post-combustion systems.

Unravelling the Influence of Perforation Sizes on Physicochemical, Sensory and Microbial Attributes of Modified Atmosphere Packaged Refrigerated Chicken Patties

ABSTRACTThe changes in lifestyle patterns and the adoption of a busy life have led to the widespread acceptance of ready‐to‐cook (RTC) products, particularly meat products. Chicken patties, being a preprocessed RTC product, are consumed worldwide. This study investigates the impact of perforated modified atmosphere packaging (MAP) on the physicochemical, sensory and microbial attributes of chicken patties stored at 4 °C for 14 days. The focus is on assessing the consequences of package damage occurring at any stage of the supply chain. Perforations measuring 0.2 mm and 0.4 mm were introduced into polypropylene packaging boxes, wherein chicken patties were subsequently packaged under five distinct gas concentrations of CO2 (ranging from 0% to 50%) and O2 (ranging from 0% to 50%), alongside a constant level of 50% N2. A control group containing normal air along with perforations was included for comparison with other groups containing modified atmospheres. The results indicated that the package (PPCON9) with the highest concentration of CO2 (50%) and the lowest O2 (0%) with 0.2 mm perforations was the most effective in preserving the quality attributes of chicken patties during storage by reducing moisture loss (3%), protein denaturation (0.38%), lipid oxidation (0.54%), cooking loss (0.72%), shrinkage (0.18%), water holding capacity (5.67%), sensory losses and microbial spoilage. The highest losses occurred in the package with 0.4 mm perforations containing normal air (PPA2). The study highlights that even if a package containing a modified atmosphere is damaged, it still efficiently preserves the chicken patties compared to a damaged package containing normal air. However, leakage significantly impacts the efficiency of MAP, necessitating effective solutions to prevent losses that occur from this issue.

Experimental Study and Process Simulation on Pyrolysis Characteristics of Decommissioned Wind Turbine Blades

The development of wind power has brought about increasing challenges in decommissioning, among which DWTBs (decommissioned wind turbine blades) are the most difficult component to deal with. To enable the cost-effective, energy-efficient, and environmentally friendly large-scale utilization of DWTBs, an experimental study on thermogravimetric and pyrolysis characteristics of DWTBs was carried out. A new process involving recycling glass fiber with pyrolysis gas re-combustion and flue gas recirculation as the pyrolysis medium was innovatively proposed, and the simulation calculation was carried out. Thermogravimetric experiments indicated that glass fiber reinforced composite (GFRC) was the main heat-generating part in the heat utilization process of blades, and the blade material could basically complete pyrolysis at 600 °C. As the heating rate increased, the formation temperature, peak concentration, and proportion of combustible gas in the pyrolysis gas also increased. The highest peak concentration of CO gas was observed, with CO2 and C3H6 reaching their peaks at 700 °C. The solid product obtained from pyrolysis at 600 °C could be oxidized at 550 °C for 40 min to obtain clean glass fiber. And the pyrolysis temperature increased with the increase in the proportion of recirculation flue gas. When the proportion of recirculation flue gas was 66%, the pyrolysis temperature could reach 600 °C, meeting the necessary pyrolysis temperature for wind turbine blade materials. The above research provided fundamental data support for further exploration on high-value-added recycling of DWTBs.

Current-Voltage (i-V) characteristics of electrolyte-supported (NiO-YSZ/NiO-SDC/ScSZ/LSCF-GDC/LSCF) solid oxide electrolysis cell during CO2/H2O co-electrolysis

Solid oxide electrolysis cells (SOECs) stabilize CO2 emissions by converting CO2/H2O into synfuel. Current-Voltage (i-V) characteristics of an electrolyte-supported button cell (NiO-YSZ/NiO-SDC/ScSZ/LSCF-GDC/LSCF) were measured as a function of temperature, water vapor concentration, and CO2 gas concentrations. The cell microstructure was characterized by the Field Emission Scanning Electron Microscope (FE-SEM). FE-SEM micrographs depict that the electrolyte layer is relatively dense, and porous fuel and air electrode layers are well adhered to the electrolyte. The i-V curves were obtained at a scan rate of 0.02 Vs−1 from 0.3 to 1.5 V. Electrolysis current density increases as the temperature increases. SOEC performance increases, but SOFC performance decreases with increased water vapor concentration. Electrolysis current densities decrease as the CO2 concentration increases. The i-V characteristics show only ohmic polarization under fuel-lean and fuel-rich conditions. At optimal conditions, current density values at 800 °C/1.5 V are -174, -187, and -195 mA cm−2 for 5 %H2O, 30 %CO2, and 30 %CO2/5 %H2O co-electrolysis. At 800 °C, open-circuit voltage (OCV) values for H2O, CO2, and co-electrolysis are 0.906, 0.891, and 0.885 V, respectively. The electrolysis area-specific resistances (ASRs) give information on the reduction of CO2 or H2O, forming CO or H2, respectively. At optimal conditions, ASR values are 3.43, 3.29, and 3.18 Ω cm2 for H2O, CO2, and co-electrolysis, respectively. Co-electrolysis has a lower ASR value than pure H2O and CO2 electrolysis, indicating that H2O and CO2 are involved in the electrochemical processes.

All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes

AbstractIn recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO2 capture using monoethanolamine as the solvent. For the validation data from the CO2 capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO2 concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.

Comprehensive Review of Modified Atmosphere Packaging for Poultry Meat: Effects on the Qualitative Characteristics and Shelf-Life Stability

The aim of this article is to review applications of modified atmosphere packaging (MAP) in poultry meat. As well as, its effects on the qualitative characteristics and shelf-life stability. Packaging is a harmonized rule of preparing the product for transportation, dissemination, retail, and end-use to ensure safe delivery. MAP is modern packaging that includes air removal (78% N2, 21% O2, and 3% CO2 gas) from inside the casings and replacing them with a single gas or a mixture of gases. The atmosphere with a high concentration of O2 by 80 % leads to off-oxygenating the dye and keeps it from deteriorating on the surface of the meat. O2 causes oxidation of fat and protein, and decreases the quality of meat. The CO2 gas concentration and the casings composition significantly affect the change in the meat pH value. The decrease in oxygen in the packaging alters the meat colour from red to purple to convert the Oxymyoglobin to Deoxymyoglobin. Moreover, the consumer rejects this type of meat with a myoglobin tint. The colour stability using MAP has improved, and the Metmyoglobin configuration rate has decreased. MAP gave less moisture loss. The total and psychrophile bacterial populations in the poultry meat treated with MAP decreased and increased with the progress of storage periods. The TBA value, the peroxide value, and the fatty acid percentage decreased by using the MAP. MAP can be a part of Hurdle's technique to prolong the chicken meat shelf life.