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Optimal locations and capacities of multiple BESSs in a RES-integrated distribution network: a real-world case study
(2026-12-01)
Khunkitti, Sirote
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Wichitkrailat, Krit
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Siritaratiwat, Apirat
The global transition toward renewable energy sources (RESs) has introduced technical challenges in distribution networks, including voltage instability, increased power losses, and peak demand fluctuations. Battery Energy Storage Systems (BESSs) provide an effective solution through voltage regulation, loss minimization, and peak shaving. However, their effectiveness strongly depends on optimal location and capacity, and a single BESS may be insufficient for network with increasing RES penetration. This study proposes an optimization framework employing the crayfish optimization algorithm (COA) to determine the optimal locations and capacities of multiple BESSs within a distribution network integrated with RESs. The objective is to minimize the total system costs, including BESS investment and performance-related costs associated with voltage deviation, transmission loss, and peak power reductions. The proposed framework is applied to a real-world system, comprising 102 buses incorporating photovoltaic (PV) and biomass distributed generation. Three installation scenarios including one, two, and three BESS units are analyzed and compared against other optimization algorithms. The results confirm the optimal BESS locations and capacities found are technically feasible for real-world deployment. Moreover, COA consistently outperforms comparative methods, particularly in cost minimization and loss reduction. Notably, the two-BESS case yields the most balanced and cost-effective performance.
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Enhancement of astaxanthin production in novel red yeast Rhodosporidiobolus sp. SP3-3/4 and genomic characterization
(2025-10-01)
Phuengjayaem, Sukanya
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Kingkaew, Engkarat
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Hoondee, Patcharaporn
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Tanasupawat, Somboon
Astaxanthin is a red carotenoid and potent antioxidant with anti-inflammatory, anti-cancer, neuroprotective, and immuno-enhancing benefits. This study focused on enhancing astaxanthin production using a novel red yeast, strain SP3-3/4, through genomic analysis. Strain SP3-3/4 shared 99.66% sequence identity in the D1/D2 domain of the large subunit rRNA gene with Rhodosporidiobolus ruineniae CBS 5001T. Whole-genome analysis indicated 82.54% similarity to R. ruineniae JCM 8097T, with average nucleotide identity (ANI) values below 80% for other strains. The ANI result, lower than the 95% cutoff, confirms SP3-3/4 as a novel species, Rhodosporidiobolus sp. SP3-3/4. Key astaxanthin synthesis genes, including CrtE, CrtYB, CrtI, CrtS, CrtR, CrtW, CrtO, and CrtZ, were annotated. Astaxanthin production was verified using HPLC and LC-MS. Optimization of medium composition and physical parameters identified optimal conditions as 20 g/L sucrose, 3 g/L yeast extract, and 5 g/L peptone, with an initial medium pH of 6.5. Maximum cell growth and astaxanthin content, reaching 9.615 g/L and 0.46 mg/g DCW, respectively, were achieved after 5 days of cultivation, corresponding to an astaxanthin yield of 6.93 mg/L and a productivity rate of 1.39 mg/L/day. Notably, a maximum productivity of 1.45 mg/L/day occurred in 3 days. These findings elucidated comprehensive optimization of astaxanthin production, incorporating functional genomic assessment and analysis of protein-encoded genes of astaxanthin-producing yeast Rhodosporidiobolus sp. SP3-3/4. Such insights represent valuable biological resources with significant implications for diverse biotechnological and bioinformatics applications.
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Sustainable tropical fruit peel waste biochars for enhanced cadmium and lead adsorption: mechanistic insights and optimization using response surface methodology and backpropagation neural networks
(2025-08-01)
Limmun, Wanida
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Limmun, Warunee
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Maneesri, Wisit
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Pewpa, Orrawan
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Chungcharoen, Thatchapol
Heavy metal contamination, particularly from cadmium (Cd(II)) and lead (Pb(II)), presents a severe environmental challenge due to its toxicity and persistence. This study explores an innovative approach by utilizing abundant yet underutilized tropical fruit peel waste to produce biochars that serve as effective, sustainable adsorbents for heavy metal remediation. Biochars derived from banana peels (BP) and Monthong durian shells (DS) were synthesized via pyrolysis at 400–800 °C and evaluated for their physicochemical properties and adsorption efficiency. The DS600 biochar exhibited the highest adsorption capacity, removing Cd(II) (40.37 mg/g) and Pb(II) (51.74 mg/g), surpassing BP600 (40.22 mg/g and 47.23 mg/g, respectively). This study introduces a dual-modeling framework by integrating response surface methodology (RSM) with backpropagation neural network (BPNN) to optimize adsorption conditions and enhance predictive accuracy. The optimized conditions achieved over 99% removal efficiency, with R2 > 0.98 and MSE < 0.05, confirming the robustness of the model-based predictions. The study highlights the superior adsorption performance of DS600 biochar, with adsorption mechanisms influenced by pH, dosage, and biochar properties. In contrast to conventional studies that focus solely on equilibrium adsorption or rely on statistical models, this work pioneers the use of tropical fruit peel biochar in heavy metal remediation, providing quantitative insights into process optimization and practical scalability. The findings demonstrate the potential for valorizing agricultural waste into high-performance adsorbents, advancing cost-effective and sustainable water treatment technologies.
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Multi-objective optimization of lignocellulolytic enzyme cocktail production from Pseudolagarobasidium acaciicola TDW-48 by artificial neural network-genetic algorithm (ANN-GA) strategy and its application in lignocellulose waste bioconversion
(2025-03-01)
Luong, Thi Thu Huong
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Poeaim, Supattra
A massive amount of lignocellulose waste is generated annually, causing many environmental concerns. The bioconversion of these wastes into value-added products by the lignocellulolytic enzymes (LCE) is one of the effective and environmental approaches. However, the use of LCE has not been extended due to high costs. This study aimed to enhance the yield of crude LCE cocktail production from Pseudolagarobasidium acaciicola TDW-48 by optimizing cultural conditions using statistical tools. Firstly, the effect of cultural factors on LCE production was identified through the Plackett-Buman design. Then, the artificial neural network-genetic algorithm (ANN-GA) strategy was applied to optimize the significant factors. The result shows that the production of carboxymethyl cellulase (CMCase), xylanase, and laccase responded differently to cultural conditions. Among these, five factors (incubation time, water content, medium pH, glucose, and CuSO<inf>4</inf> concentration) were identified to have significant effects on enzyme activities. The ANN-GA optimization with a neuron network architecture (5-23-3) successfully modeled the crude LCE cocktail production, where the R-value achieved 0.98369 for the total dataset. A set of optimum conditions was proposed with an incubation time of 8 days, 72.6% water content, medium pH at 2.97, 0.5% glucose, and 0.53 g/L of CuSO<inf>4</inf>. With the above conditions, P. acaciicola TDW-48 could produce 23.97 U/g of CMCase, 26.02 U/g of xylanase, and 139.11 U/g of laccase, which enhanced 14.4%, 8.7%, and 405% activity, respectively, compared with non-optimization. In addition, the P. acaciicola TDW-48’s crude LCE cocktail performed a high bioconversion efficiency on lignocellulose waste, the reducing sugar yield achieved 327.29 mg/g on rice straw, 308.02 mg/g on rice husk, and 312.29 mg/g on corn stover after 8-h incubation. These results provided a highly effective approach for LCE production with multi-objective optimization based on an artificial intelligence platform and supported the reuse of lignocellulose waste toward the eco-friendly strategy.
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Numerical exploration of Hall and Dufour effects on rotating MHD natural convection near an infinite vertical plate with ramped boundary conditions using FDM and RSM using combined FDM and RSM approaches
(2025-03-01)
Mopuri, Obulesu
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Ganteda, Charankumar
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Palegari, Rudraravi Kumar
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Jaya Lalitha, G.
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Harikrishna, P.
This study investigates the effects of ramped parameters, diffusion thermo effects, radiation and heat absorption, Soret, and Hall effects on rotating MHD free convective flow under simultaneous ramped boundary conditions. The governing equations are transformed into dimensionless form and solved using an explicit finite difference method (FDM), with numerical results for velocity, temperature, concentration, viscous drag, heat, and mass transfer rates analyzed using MATLAB. Results show that increasing ramped parameters enhances momentum, heat, and mass transfer rates, with a novel observation of increased fluid velocity under stronger magnetic constraints. Additionally, the finite response method (FRM) is proposed to optimize parameter interactions, enabling efficient modeling and prediction of outcomes for variations beyond those tested in the FDM. This integration enhances understanding of sensitivities and optimal conditions in fluid behavior under simultaneous ramped constraints.
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Enhancing antioxidant extraction efficiency from red dragon fruit peel by green approach using novel optimization technique
(2025-01-01)
Loan, Le Thi Kim
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Thao, Le Thi Nhu
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Vinh, Bui The
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Mansamut, Chaiyut
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Tai, Ngo Van
This study is the first application of a combined sonication and enzyme extraction technique as green technology to recover biological compounds from the peel of red-fleshed dragon fruit, utilizing Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). The peel of dragon fruit had sonication pretreatment for 10–30 min (X<inf>1</inf>), followed by hydrolysis using 0.1 % Pectinex Ultra SP-L enzyme at temperatures ranging from 30 to 60 °C (X<inf>2</inf>) for a duration of 60–120 min (X<inf>3</inf>). The Box-Behnken design was employed to structure the experiment. The levels of polyphenol, betacyanin, and antioxidant activity in the extract were utilized to assess the efficacy of the extraction method. The research demonstrated a substantial enhancement in efficiency by the application of ultrasound pretreatment during the enzymatic hydrolysis of dragon fruit peel. The study identified the ideal parameters for the extraction process using the ANN-GA approach, which include an ultrasonic duration of 27.5 min, an enzyme incubation temperature of 47.1 °C, and an enzyme incubation duration of 135.1 min. Under these conditions, the extract exhibited a total phenolic content of 165.34 mg GAE/g peel weight, betacyanin content of 131.87 mg/100 g peel weight, and an antioxidant activity of 0.92 mg TE/100 g by using DPPH radical scavenging activity assay. The research demonstrated that dual treatment enhances the extraction process of chemicals from by-products, particularly dragon fruit peel. The study established a foundation for future research on the utilization and integration of effective extraction technologies to enhance the quality of extracts for use in the food sector.
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Predictive Modeling and Optimization of Biogas Reforming and Proton-Conducting SOFCs Integrated System
(2025-01-01)
Patcharavorachot, Yaneeporn
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Saebea, Dang
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Arpornwichanop, Amornchai
In this study, the power generation performance of a proton-conducting solid oxide fuel cell (H-SOFC) integrated with biogas steam reforming is investigated to determine the optimal operating conditions. The system design and process simulation are carried out using Aspen Plus. The effects of three key operating parameters - reformer temperature, steam-to-biogas (S/C) molar ratio, and SOFC operating temperature - on electrical performance and CO emissions are examined. Predictive modeling is developed using a Regression Tree to capture the relationship between input parameters and performance indicators. Subsequently, a Genetic Algorithm (GA) is employed to identify the optimal operating conditions that maximize power output and SOFC efficiency while minimizing CO emissions. The results indicate that the optimal reformer temperature is 1024.26 K with an S/C ratio of 1.5, and the H-SOFC should operate at 1024.53 K, yielding a power output of 427.60 kW, an SOFC efficiency of 43.53%, and CO<inf>2</inf> emissions of 226.62 g/kWh. This demonstrates that the integrated system provides a highly efficient and low-carbon power generation solution.
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Intelligent model and optimization of ultrasound-assisted extraction of antioxidants and amylase enzyme from Gnaphalium affine D. Don
(2025-01-01)
Luangsakul, Naphatrapi
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Kunyanee, Kannika
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Kusumawardani, Sandra
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Ngo, Tai Van
The study uses ultrasound-assisted extraction to recovery the antioxidant and amylase enzyme from Gnaphalium affine D. Don, namely “chewcut” in Thailand. The study involves two statistical methods: artificial neural networks (ANN) and response surface methodology (RSM) to model and optimize extraction procedure for improving the yield of antioxidant and amylase enzyme activity (AEA). Both RSM and ANN showed the potential to predict and find the optimal extraction conditions. However, ANN model could give more accurate values compared with validation test. ANN model found that under optimal conditions (temperature: 65.92 °C, ultrasonic power: 58.22 %, extraction time: 37.95 min), the total phenolic compounds, total flavonoid compounds, antioxidant activity and AEA were 218.35 ± 0.34 mgGAE/g, 0.554 ± 0.045 mgQE/g, 84.2 ± 0.2 %, 364.14 ± 1.35 mg-maltose/g. This is the first report on amylase potential of chewcut, which could be further served as the natural enzyme source. Moreover, by adding its bioactive compounds, it may be possible to improve nutraceutical properties and quality of products.
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Impact of foam-mat drying conditions of “Gấc” aril on drying rate and bioactive compounds: Optimization by novel statistical approaches
(2024-12-30)
Thuy, Nguyen Minh
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Tien, Vo Quoc
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Giau, Tran Ngoc
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Hao, Hong Van
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Minh, Vo Quang
This study was conducted to optimize the foam-mat drying conditions to maximize quality [β-carotene and total polyphenol content (TPC)] and drying rate of “Gấc” aril powder by using two novel statistical techniques as Response Surface Methodology (RSM) and Artificial Neural Network (ANN) couple with Genetic Algorithm (GA). During production process, level of egg albumin (EA) used for foaming process and drying temperature mainly influenced the drying rate and content of antioxidant compounds in powder. ANN model of 3–10–3 showed more accuracy and faster prediction capacity than RSM model did. ANN-GA model predicted the optimal conditions to be 13.31 % EA, 0.26 % xanthan gum and drying temperature of 73.1 °C, with the drying rate of 1.89 g-water/g-dry matter/min, β-carotene content of 395.88 μg/g, TPC of 1.68 mgGAE/g. These results confirmed the suitability and promising of foam-mat drying for “Gấc” aril powder production, to be producing food ingredient containing highly bioactive compounds.
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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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