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Mechanical properties of self compacting concrete reinforced with hybrid fibers and industrial wastes under elevated heat treatment
(2025-12-01)
Onyelowe, Kennedy C.
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Hanandeh, Shadi
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Zurita Polo, Susana Monserrat
Machine learning prediction of the mechanical properties of self-compacting concrete (SCC) reinforced with hybrid fibers, incorporating industrial wastes like fly ash and blast furnace slag, and cured under elevated heat provides a reliable and efficient alternative to traditional laboratory experiments. In this work, extensive literature review leading to the collection, sorting and curation of a global database representative of the mechanical properties of self-compacting concrete reinforced with hybrid fiber mixed with industrial wastes for sustainable construction was conducted. The collected database constituted traditional concrete components and admixtures such as Cement (C), Fly ash (FA), Slag (BFS), Fine Aggregate (FAg), Coarse Aggregate (CAg), Water (W), Superplasticizer (PL), Fiber (Fi), and Temperature (Temp.) studied under the mechanical properties such as the Compressive Strength (Fc), Tensile Strength (Fsp), and Flexural Strength (Ff). The collected 114 records were divided into training set (90 records = 80%) and validation set (24 records = 20%) following the guidelines for data partitioning for optimal performance in machine learning predictions. Different advanced machine learning methods created using “Weka Data Mining” software version 3.8.6 were applied such as “Semi-supervised classifier (Kstar)”, “M5 classifier (M5Rules), “Elastic net classifier (ElasticNet), “Correlated Nystrom Views (XNV)”, and “Decision Table (DT)” to predict the output. The Hoffman/Gardener and SHAP techniques are used to estimate the sensitivity of the input parameter on the output. Finally, various performance metrics are used to evaluate the reliability of the models. The results show that the machine learning models show varying degrees of predictive accuracy, with the Kstar and XNV models consistently outperforming others across all mechanical properties. However, Kstar with accuracies of 96.5%, 96.0%, and 97.0% for Fc, Fsp, and Ff predictions, respectively proposed the most decisive model. Also, the Hoffman and Gardener method highlights the role of the binders, chemical additives, and curing, whereas SHAP attributes greater importance to aggregates and binder interactions.
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Mathematical and artificial neural network modeling for describing the infrared drying process of Moringa oleifera leaves and evaluation of product quality
(2025-08-01)
Thuy, Nguyen Minh
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Hao, Hong Van
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Giau, Tran Ngoc
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Minh, Vo Quang
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Tai, Ngo Van
Moringa oleifera leaves were used in the infrared drying method for powder production. The moisture ratio datasets during drying at different temperatures were fitted with eight thin-layer drying kinetics and analyzed by an artificial neural network (ANN). The goodness of fit was evaluated using the value of the coefficient of determination (R2), the chi-square (χ2), and the root mean square error (RMSE). Results indicated that drying time was between 40 and 95 min at a temperature of 55 to 70 °C. Among the mathematical drying models used, the Wang and Singh model best described the drying kinetics of Moringa leaves. But comparing with the ANN model—a machine learning-based model—it showed higher prediction capacity than the mathematical model did. For Moringa leaves dried at temperatures between 55 and 70 °C, the R2, χ2, and RMSE values for this model ranged from 97.85 to 99.59%, 0.0007 to 0.0029, and 0.0228 to 0.0503, respectively. Effective moisture diffusivity (D<inf>eff</inf>) values varied between 1.908 × 10−11 and 3.875 × 10−11 m2/s, with an activation energy of 43.92 kJ/mol. The drying temperature also influenced the bioactive compounds in Moringa leaves. The vibrant color of the powder was produced by drying Moringa leaves at 65 °C for 50 min. The powder had 5.85% moisture, 31.97% protein, 61.05 mg/100 g β-carotene, 62.82 mg QE/g total flavonoid content, and 1789.65 mg/100 g calcium content.
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Application of foam-mat drying to produce field crab powder: Foaming process optimization, drying kinetics, and final product characterization
(2025-08-01)
Thuy, Nguyen Minh
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Nhut Minh, Ngo Ngoc
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Kha, Nguyen Hoang
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Bich Thuy, Bui Thi
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Giau, Tran Ngoc
This study used foam-mat drying to make powder from field crab meat for the first time. In which, the effect of foaming conditions [egg albumin (EA, 7.96–16.44 %) and xanthan gum (XG, 0.04–0.44 %)] and drying temperature (65–80 °C) on powder quality were examined. With appropriate EA and XG levels of 13.16 % and 0.30 %, foam density and foam expansion were 0.395 g/mL and 279.78 %, respectively. The total energy required and specific energy consumption decreased. In contrast, thermal efficiency and energy efficiency rose with drying temperature, reaching 2.494–4.452 %, and 1.419–1.879 %, respectively. Temperature affects effective moisture diffusion coefficient according to the Arrhenius equation with an activation energy of 39.33 kJ/mol. Fitting experimental data to seven thin-layer drying models and an ANN model. The Aghbashlo model scored best, with the highest correlation coefficient. Nevertheless, the ANN model demonstrated slightly superior accuracy compared to the Aghbashlo model, indicating the potential for industrial system control.
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Assessing adulterated pineapple juice concentrate using electrical properties
(2025-01-01)
Tantinantrakun, Achiraya
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Sinsamut, Varisara
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Apairat, Nuengruthai
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Smutrakalin, Thirapol
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Thompson, Anthony Keith
The fraudulent addition of sugars to pineapple juice concentrate undermines consumer trust and satisfaction. Resistance (R), capacitance (C), dissipation factor (D), inductance (L), quality factor (Q), impedance (Z) and phase angle (θ) in the range of 0.012–200 kHz of juice adulterated with sugar increasing levels from 0 to 95% at 0.5% (w/w) intervals were tested to determine whether they could be used for detecting adulteration in pineapple juice concentrate using a LCR (inductance, capacitance, resistance) meter. A multiple linear regression (MLR) model was developed for predicting the concentration of additive sugars in samples. Linear discriminant analysis (LDA) was used for classifying pure pineapple juice concentrate and pineapple juice concentrate adulterated with added sugars. The most accuracy in the MLR model was obtained from θ, which achieved a correlation coefficient of prediction (R<inf>p</inf>) of 0.977 and a root mean square error of prediction (RMSEP) of 5.88% w/w. From the LDA analysis, the most accurate parameter for classification was C, which yielded a predictive classification accuracy of 94.57%. Therefore, this technique indicates its potential for use in the fruit juice industry a simple method for routinely testing in order to ensure the non-contamination of products offered for sale to consumers.
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Effect of foaming conditions and drying temperatures on total polyphenol content and drying rate of foam-mat dried banana powder: Modeling and optimization study
(2024-12-01)
Van Tai, Ngo
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Van Hao, Hong
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Han, Tran Thi Ngoc
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Giau, Tran Ngoc
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Thuy, Nguyen Minh
This study aims to optimize the parameters of the foam-mat drying to produce banana powder. Input parameters of the foam drying such as foaming agent (egg albumin) concentration used from 5 to 15 %, the foam stabilizer (maltodextrin) used from 1 to 3 % and the drying temperature varied between 60 and 80 °C. Factors with 3 levels are arranged according to the Box-Behnken design, which further modelled by RSM (response surface methodology) and ANN (artificial neural network), and optimized. The drying rate and total polyphenol content (TPC) of banana powder under the studied conditions were determined as the target output. The moisture, color, total polyphenol, antioxidant activity and some physical parameters of final fine powder were analyzed. Increasing temperature has increased the drying rate. In addition, increasing the concentrations of egg albumin and maltodextrin maintained the highest TPC and maximum drying rate. ANN model showed the higher forecasting capacity than that of RSM. Moreover, simultaneous optimization of two responses (TPC and drying rate) was selected to maximize the desired value at the concentration of albumin, maltodextrin and drying temperature of 11.86 %, 1.92 %, 74.94 °C, respectively, corresponding to the highest TPC value and drying rate of 1.31 mgGAE/g DW and 2.48 g water/g dry matter/min. At this condition, the drying time was recorded as 103 min. Validation of the optimal ratios showed that the experimental values of TPC and drying rate were in good agreement with the model predicted data. The moisture content and water activity of product were found to be 5.87 ± 0.07 % and 0.37 ± 0.01. The product had bright colors with L*, a* and b* values were measured as 86.4 ± 0.5, 1.75 ± 0.08 and 15.2 ± 0.3, respectively. The high DPPH radical scavenging activity was detected (53.5 %) with the water solubility index and water absorption index of banana powder was determined at value of 56.98 % and 5.17 g/g, respectively. Foam mat dried banana powder was well preserved in paper packaging with aluminum foil.
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Overview of biorefinery
(2022-01-01)
Thongchul, Nuttha
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Charoensuppanimit, Pongtorn
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Anantpinijwatna, Amata
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Gani, Rafiqul
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Assabumrungrat, Suttichai
A strong reliance on fossil resources gives rise to a depletion of nonrenewable resources and negative or harmful environmental impacts. Circumvention of this energy-environment nexus has been proposed through the application of the concept of biorefinery. In this concept, biomass, an alternative renewable feedstock containing C-rich chemicals, is utilized as a replacement of the fossil-based feedstock to produce bioenergy and bio-based chemicals. Originally, biorefinery was perceived as a platform of biomass processing, which would produce primarily fuels and chemicals. To date, biorefinery harnesses a variety of sustainable and synergetic technologies that converts biomass into a wide range of profitable products such as food-and-feed for the future, biopharmaceuticals, and nutraceuticals. Due to variability of feedstock and newly emerged technologies, classifications of biorefinery are diverse and depend on the basis (e.g., source of a biomass, the generation of a feedstock, etc.) taken in consideration. A comprehensive view of biorefinery requires the consideration of processing of biomass from different origins via diversified technology platforms. Since the concept of biorefinery also concerns social aspects and location-specific technologies, various aspects of stakeholders including academia, industry, economy, and society need also to be considered. Collaboration among the various actors is facilitated if necessary key information is easily accessible. Therefore, an overview of biorefinery should cover key information related to biorefinery, such as nature of biomass, current situation, available technologies, process design methods, associated tools, and analyses of processing routes along with case studies. In this chapter, the indices representing the key information related to biorefinery are arranged alphabetically and tabulated to enhance a good understanding of the concept of biorefinery.
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Non-equilibrium numerical modeling for combustion of LPG within porous media
(2019-11-01)
Wasinarom, Kittipass
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Charoensuk, Jarruwat
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Lilavivat, Visarn
A numerical model for lean premixed combustion of LPG (70% propane and 30% butane) within a porous inert medium was developed. Experiments were conducted at three different firing rates at the equivalent ratios of 0.4 and 0.6. The model was developed with the thermal non-equilibrium concept between phases and validated with three cases of experimental results. The discussion of model calibration was undertaken by focusing on the effects of the extinction coefficient and convection heat transfer effective area. Comparisons were made of the temperature profile, as well as the peak temperature, with the calculated adiabatic temperature. The model agreed well with experimental results and was robust throughout three firing rates. Moreover, it was found that the two aforementioned thermal parameters had different roles in temperature distribution, which provided insight on flame front location and heat transfer between phases within the porous domain.
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Experiment and numerical modeling of stratified downdraft gasification using rice husk and wood pellet
(2019-01-01)
Wasinarom, Kittipass
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Charoensuk, Jarruwat
Stratified downdraft gasification using rice husks and wood pellets was carried out under different air mass flow rates using both experimental and numerical methods. The flame propagation rate was calculated from the temperature profile at different time steps and was used as the prerequisite to calculate the equivalent ratio in modeling the combustion zone. Chemical equilibrium modeling was employed to predict the temperature and composition of the sample in the combustion zone. Finite kinetic modeling was used to simulate the reduction zone. The initial temperature and composition of the reduction zone simulation were obtained from the chemical equilibrium results taken from the combustion zone. The flame propagation speed of the rice husk was found to be around five times greater than wood pellet at the same air flow rate. The peak temperature of both fuels had similar values. For all air mass flow rates, the equilibrium modeling over-estimated the peaks in comparison with the experimental tests. The kinetic model was sensitive to the input temperature at the zone inlet. The predicted temperature in the reduction zone demonstrated high kinetic activity at the top of the zone due to a high gas temperature. The predicted temperature was in agreement with the experimental test results.
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Study of ethanol fermentation reaction using Saccharomyces diastaticus in a two-tank fermentation system with cell recycling
(2018-10-01)
Lerkkasemsan, Nuttapol
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Lee, Wen Chien
Experimental data of ethanol fermentation in a two-tank system with cell recycling using sucrose as a substrate were investigated to establish a kinetic model that described the reaction. Flocculent yeast Saccharomyces diastaticus LORRE-316 was used as the fermenting yeast, and the fermentation medium comprised 80 g/L sucrose, 10 g/L yeast extract, 10 g/L peptone, 2 g/L potassium phosphate monobasic (KH<inf>2</inf>PO<inf>4</inf>), and 0.5 g/L magnesium sulfate heptahydrate (MgSO<inf>4</inf>·7H<inf>2</inf>O). Three models, the Monod model, the modified Monod model, and the extended-modified Monod model, were used to describe fermentation, and the extended-modified Monod model described the reaction more accurately than the other two models. The model took substrate limitations plus substrate and ethanol inhibitive effects into consideration, and was modified to include assumptions that included terms for substrates (sucrose, glucose and fructose) and product (ethanol). In addition, the ethanol concentration had a significant effect on cell growth. The results of Lineweaver–Burk plots showed that the maximum specific growth rate (μ<inf>MAX</inf>) and the Monod constant (K<inf>S</inf>) were 0.72/h and 26.77 g/L, respectively. The models were suitable for describing the experimental data.
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Flowsheet-based model and exergy analysis of solid oxide electrolysis cells for clean hydrogen production
(2018-01-01)
Im-orb, Karittha
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Visitdumrongkul, Nuttawut
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Saebea, Dang
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Patcharavorachot, Yaneeporn
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Arpornwichanop, Amornchai
A solid oxide electrolysis cell (SOEC) is an electrochemical technology used for hydrogen production via a steam electrolysis reaction. Because the existing SOEC models are complicated, the aim of this study is to develop a user-friendly SOEC model in a flowsheet simulator (Aspen Plus). The developed model is used to perform a parametric analysis to investigate the effects of key process parameters, i.e., operating temperature, current density, steam concentration, sweep gas type and number of cells, on the SOEC performance. The simulation results show that the voltage and the overall overpotential decrease as the cell temperature increases, whereas the opposite trends are observed when the current density increases. From the energy and exergy analyses, the total energy demand slightly increases with cell temperature, whereas the electrical energy demand decreases. Based on an operating temperature of 1273 K when the SOEC uses oxygen as the sweep gas, the highest energy and exergetic efficiencies of 78.45% and 92.20% are achieved at a current density of 2500 A m−2 and at a steam concentration of 90% in a 500-cell stack.

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