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An innovative chitosan-coated aquatic feed pellets production from coastal waste using top-spray fluidized bed drying
(2026-12-01)
Maikaew, Jatuphat
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Srisang, Naruebodee
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Tambunlertchai, Supreeda
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Srisang, Siriwan
Coastal wastes such as crab shells, shrimp shells, and seaweed are rich in proteins, lipids, and bioactive compounds, making them valuable raw materials for aquafeed production. In this work, three aquatic feed pellets were developed and tested under different drying temperatures from 70 to 110 °C to evaluate the pellet durability index (PDI), specific energy consumption in water removal (SEW), and nutrient quality. The formulation containing high crab shell content showed the most balanced nutritional profile but required further improvement in mechanical strength. To address this, chitosan coating was applied using a top-spray fluidized bed system, with process conditions optimized through response surface methodology (RSM). The RSM demonstrated the optimal coating condition at a concentration of about 1.25% (w/v), a spray rate of about 32.5 mL/min, and a temperature of about 110 °C, with the lowest of drying time (DT) and specific energy consumption (SEC). The optimized coating significantly improved PDI and water solubility index while preserving nutritional balance. It also enhanced antimicrobial properties, which are desirable for feed storage. Microscopic and structural analyses confirmed good adhesion of the coating. Overall, this study demonstrates a sustainable pathway to convert coastal waste into high-quality aquafeed, offering both environmental benefits and practical value for aquaculture industries.
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Mineral filler characteristics and non-Newtonian viscosity of asphalt mastic at high temperature: A response surface methodology approach
(2026-12-01)
Chamwon, Suwaphit
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Hutabarat, Multazam
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Chaturabong, Preeda
Asphalt mastic—composed of asphalt binder and mineral filler—strongly governs the high-temperature viscosity of asphalt concrete during mixing and compaction. This study investigates the effects of four mineral fillers (granite, limestone, shale, and pumice) on the viscosity and flow behavior of asphalt mastics at elevated temperatures (130–170 °C). Comprehensive filler characterization was performed, encompassing morphological analysis by scanning electron microscopy (SEM), physical property evaluation (particle density, specific surface area, and Rigden voids), hydrophilic coefficient determination, particle size distribution by laser diffraction, and mineralogical identification by X-ray diffraction (XRD). Response surface methodology (RSM) with a central composite design (CCD) was employed to model the combined effects of temperature (130–170 °C), filler content (5–30% by volume), and rotational speed (10–30 RPM) on apparent viscosity measured by a Brookfield rotational viscometer. The non-Newtonian index, derived from the power-law (Ostwald–de Waele) model, was used to classify flow behavior. Results demonstrate that temperature and filler content are the dominant parameters governing viscosity, while rotational speed controls the degree of non-Newtonian behavior in a filler-dependent manner. A critical transition from quasi-Newtonian to pronounced non-Newtonian flow was identified at 15–20% filler content by volume, coinciding with a sharp increase in the effective solid volume fraction that approaches the colloidal packing threshold (φ* ≈ 32–34%). Pumice exhibited the strongest shear-thickening (n up to 1.22) while granite showed consistent shear-thinning (n ≈ 0.88–0.94), with limestone and shale showing mixed behavior. Pumice and shale produced the strongest viscosity stiffening, consistent with greater binder immobilization due to their higher Rigden voids. AASHTO T316 workability analysis indicates that under base-binder conditions, the maximum filler content within practical mixing limits at 160 °C is approximately 30% for granite and limestone but only ∼22% for pumice. The validated quadratic RSM models (R² = 0.84–0.96) provide a practical framework for optimizing filler selection and processing temperatures in asphalt mixture design.
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Biogeosynthetic recycling of iron-ore tailings for green stabilization of expansive soils
(2026-07-01)
Mehmood, Mudassir
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Nie, Wen
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Liu, Yunlong
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Onyelowe, Kennedy
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Jalal, Fazal E.
Expansive soils pose a significant challenge to civil infrastructure due to their high potential for expansion and contraction. These soils exhibit poor mechanical properties, leading to severe structural damage and high maintenance costs. To address these challenges, conventional stabilization like cement or lime, are widely used; however, their production substantially increases global carbon dioxide emissions and energy requirements. Therefore, there is an urgent need to develop sustainable alternatives that enhance soil performance while minimizing environmental impact by utilizing industrial by-products. In response to this need, this study proposes a sustainable composite reinforcement scheme that combines enzyme-induced carbonate precipitation (EICP), sisal fiber (SFs) reinforcement, and iron ore tailings (IOts) to treat expansive soil by deploying laboratory testing and response surface modeling (RSM). Utilizing the experimental and validated optimal mix (0.75 mol/L EICP + 0.53 % SFs + 11.7 % IOts) reduced swelling pressure ∼98 % while increasing the unconfined compressive strength ∼262 %, cohesion ∼78 %, the angle of internal friction ∼172 %, Unsoaked California Bearing Ratio (CBR<inf>unsoak)</inf> from 2.4 % to ∼26 % and CBR<inf>soak</inf> 1.7 % to ∼20 % after 28 days curing. In addition, SEM and EDS analyses confirmed synergistic microstructural interactions, resulting in a highly reinforced soil composite. Moreover, the RSM model showed good agreement with the experimental results, with errors controlled within ±5 %, validating the robustness of the model. By reusing mining waste and utilizing renewable fibers, this approach demonstrates a low-carbon, cost-effective, and scalable stabilization strategy that enhances infrastructure resilience and promotes circular economy objectives.
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Sustainable magnetic biochar from agro-aquacultural waste for efficient Pb(II) and Cd(II) removal: Machine learning–assisted optimization and techno-economic evaluation
(2026-05-15)
Limmun, Wanida
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Limmun, Warunee
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Prangmoo, Yasumin
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Ishikawa, Nao
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Borkowski, John J.
This study presents the sustainable synthesis and optimization of magnetic biochar derived from agro-aquacultural waste, specifically rubber seed shells and oyster shells (MRO), for the efficient removal of Pb(II) and Cd(II) from aqueous solutions. MRO was synthesized via FeCl<inf>3</inf> activation and co-pyrolysis, enhancing adsorption capacity and magnetic recoverability. Process optimization was performed using Response Surface Methodology (RSM) and a Genetic Algorithm–Backpropagation Neural Network (GA–BPNN), with experimental validation confirming RSM-predicted conditions. The optimized MRO achieved high adsorption capacities of 709.99 mg/g for Pb(II) and 332.98 mg/g for Cd(II), following Langmuir and pseudo-second-order kinetic models. Mechanistic analysis identified surface complexation, ion exchange, electrostatic interaction, and precipitation as key pathways. MRO demonstrated excellent reusability, maintaining over 90% Pb(II) removal efficiency after 11 regeneration cycles. Techno-economic and environmental assessments revealed a low production cost (23 THB/kg), modest energy consumption (2.5 kWh/kg), and a reduced carbon footprint (0.50 kg CO<inf>2</inf>/kg). These results underscore the potential of MRO as a cost-effective and scalable adsorbent for sustainable wastewater treatment applications.
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Optimization of astaxanthin production by Rhodotorula toruloides CB6-10/1 using response surface methodology and its genome analysis
(2026-02-01)
Butsararattanagomen, Pornthipa
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Tanasupawat, Somboon
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Kingkaew, Engkarat
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Kotatha, Ditpon
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Soontorngun, Nitnipa
Astaxanthin is a valuable carotenoid with potent antioxidant properties and has broad applications in the pharmaceutical, nutraceutical, and cosmetic industries. In this study, Rhodotorula toruloides CB6-10/1, isolated from Canna indica L. flowers, was evaluated for astaxanthin production. The orange-red pigment was confirmed as astaxanthin via thin-layer chromatography and high-performance liquid chromatography, with quantification performed by spectrophotometry. Comprehensive genome analysis and production optimization of R. toruloides CB6-10/1 confirmed the presence of key astaxanthin biosynthesis genes, such as CrtYB, CrtI, CrtW, CrtZ, and CrtR, which facilitate the conversion of β-carotene to astaxanthin through hydroxylation and ketolation. The key parameters, including carbon and nitrogen sources, their concentrations, trace elements, agitation speed, and pH, were systematically evaluated to optimize production. Although copper appeared beneficial in the Plackett–Burman screening, its effect and those of other metals were not significant. Optimization using Response Surface Methodology for cost-effective nitrogen sources determined that a combination of 1.10 g/L yeast extract, 10.0 g/L peptone, and 0.50 g/L ammonium sulfate yielded the maximum astaxanthin production of 4.728 mg/L, under cultivation conditions of pH 4.5, 200 rpm, and 30 g/L glucose, representing a fourfold increase compared with the basal medium. This optimization not only enhances pigment production efficiency but also reduces dependency on costly trace elements, improving process scalability and economic feasibility. Overall, these results demonstrate R. toruloides CB6-10/1 as a promising microbial source for sustainable astaxanthin production with potential further applications.
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Fabrication of Eco-Friendly Pineapple Leaf Fiber-Based Vegan Leather for Environmental Sustainability
(2026-01-20)
Srisang, Siriwan
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Kunyuan, Jumpon
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Hutangkoon, Thunyanat
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Eiangmee, Orranat
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Maikaew, Jatuphat
The environmental impact of synthetic leather production has raised global concerns due to its reliance on petroleum-based polymers and poor biodegradability. Therefore, the development of sustainable, eco-friendly alternatives using renewable resources has become increasingly important. A biodegradable vegan leather was developed from natural rubber and pineapple leaf fibers (PALF), with properties analyzed using Response Surface Methodology (RSM). The effects of fiber content (X1), compression time (X2), and compression temperature (X3) were studied on biodegradation (Y1), water absorption (Y2), and tensile strength (Y3). Results showed that all three factors significantly influenced Y1, with the predictive model demonstrating high reliability (R > 90%). The optimum condition for Y1 was X1 = 3.0 g, X2 = 70.0 min, and X3 = 110.0 C, yielding a maximum predicted biodegradation of about 21%. In contrast, the models for Y2 and Y3 were statistically unreliable (P > 0.05) due to low RÂ² values. However, Y2 passed the lack-of-fit test, suggesting an adequate model form, while Y3 failed (P < 0.05), indicating an inadequate prediction model. These findings suggest future experiments should narrow factor ranges and include additional control variables to improve the predictability of Y2 and Y3. Despite these limitations, the study highlights a sustainable alternative to conventional synthetic leather, aligning with circular economy principles and supporting the United Nations Sustainable Development Goals (SDGs). Importantly, the process is resource-efficient: from 1 kg of pineapple leaves, only 20 g of fibers is obtained, and just 75 g of PALF was used in 15 experimental runs. This minimal material requirement underscores the potential of the approach for sustainable production, while highlighting potential applications in sustainable fashion, packaging, and eco-friendly product design.
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Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis
(2025-12-01)
Onyelowe, Kennedy C.
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Ebid, Ahmed M.
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Awoyera, Paul
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Kamchoom, Viroon
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Rosero, Evelin
Recent sustainable engineering trends show the re-use of wastes in the production of concrete materials. This was important in two ways. First, there is a great environmental necessity to eliminate these industrial wastes and their usage in a solid waste upcycling system to ensure structural sustainability creates an avenue for this process. Second, it has become important to reduce laboratory and equipment costs by establishing intelligent models through the application of these supplementary cements and optimized for optimal performance of concrete materials. For these reasons, the present research work has applied the intelligent learning abilities of eight (8) ensemble-based and one (1) symbolic regression machine learning methods to predict the strengths (compressive-Fc, flexural-Ff and splitting tensile-Ft) of SCGPC with the “Orange Data Mining” software version 3.36. In this research paper, the influence of the industrial wastes like ground granulated blast furnace slag (GGBS) and fly ash (FA) and alkali activators such as (NaOH and Na<inf>2</inf>SiO<inf>3</inf>) on the performance self-compacting geopolymer concrete (SCGPC) in terms of strength has been studied. This has been executed using 132 mix entries at different curing regimes and partitioned into 75% and 25% for training and validation, respectively. At the end of the process, performance indices were employed to test accuracy and comparatively the best. Also, the Taylor chart-based comparison of the performance of the ensemble-based machine learning models was conducted. The results show that for the compressive strength of the SCGPC (Fc) model, the K-NN outclassed all the ensemble techniques with an average R2 of 0.99, accuracy of 0.96, and an average error of 0.04%. This is followed in order of superiority by the SVM closing its model with an average R2 of 0.99, accuracy of 0.955 and average error of 0.045%. Both models ended with equal SSE, MAE, MSE, and RMSE. For the flexural strength of the SCGPC (Ff) model, the K-NN and the SVM performed equally in all the studied indices especially with average R2 of 0.99 and outclassed all the other ensemble techniques. Finally, for the splitting tensile strength (Ft) of the SCGPC, the K-NN and the SVM again performed equally with an average R2 of 0.985 and the other performance indices. The RSM showed a strong competition with R2 (standard = 1) of 0.987, 0.973, and 0.986 and adequate precision (standard = 7) of 129.7, 85.3, and 123.5, for the Fc, Ff, and Ft, respectively. In addition, the RSM as a symbolic regression learning system proposed closed-form equations with which the model can be applied manually to design the production of SCGPC performing at optimal strength with optimized application of the industrial waste materials especially the most influential components, which are the GGBS, FA and NaOH. The NB came least in performance. Overall, the studied ensemble-based ML techniques applied in this present research paper outperformed the techniques used in previous literatures, except the poorly performed NB.
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Magnetic calcium silicate hydrate–oyster shell waste nanocomposite for phosphate removal and recovery: RSM-based optimization, mechanism, and real water application
(2025-09-01)
Boonkanon, Chanita
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Wongniramaikul, Worawit
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Phawachalotorn, Chanadda
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Limwongsakorn, Somsak
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Choodum, Aree
Magnetic nanocomposites offer an effective strategy for phosphate removal from wastewater, preventing eutrophication and enabling phosphate recovery for reuse as fertilizer—supporting a zero-waste approach. In this study, a novel hybrid nanocomposite composed of magnetized calcined oyster shell waste and calcium silicate hydrate (M-COS-CSH) was synthesized through a simple process completed within 5.2 h. Response Surface Methodology was employed for optimization: a Central Composite Design determined the optimal FeCl₃ and COS ratios, while a Box–Behnken Design optimized adsorption conditions including adsorbent dose (20–100 mg), initial phosphate concentration (10–90 mg L−1), contact time (15–75 min), and pH (3−11). Under optimal conditions (60 mg adsorbent, 10 mg L−1 phosphate, 45 min, pH 6.28), M-COS-CSH achieved a predicted maximum removal efficiency of 97.30 %. An experimental removal efficiency of 98.06 % ± 0.19 % was obtained under the same conditions without pH adjustment (pH 6.84), offering a cost advantage. The adsorption process closely followed the Langmuir isotherm model (R2 = 0.9963), with a maximum adsorption capacity of 161.29 mg g−1, and was best described by the pseudo-second-order kinetic model (R2 = 1.0000). Characterization suggested a mechanism involving surface microprecipitation and inner-sphere complexation. Thermodynamic analysis confirmed the process to be endothermic and spontaneous (ΔG°: −9.97 to −10.84 kJ mol−1; ΔH°: 3.00 kJ mol−1; ΔS°: 43.52 J mol−1 K−1). M-COS-CSH achieved phosphate removal ranging from 70.81 % ± 2.47 % to 94.24 % ± 0.39 % in real water samples. Even in the presence of competing anions at fivefold phosphate concentration, removal efficiency remained high (93.58 % ± 0.32 %), confirming the material's strong selectivity and suitability in complex matrices.
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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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Optimization and performance prediction of carbon dioxide adsorption on chitosan/activated carbon/epichlorohydrin composite materials using Box–Behnken design and artificial neural network approaches
(2025-06-01)
Loryuenyong, Vorrada
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Nakhlo, Worranuch
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Srikaenkaew, Praifha
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Yaidee, Panpassa
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Eiad-Ua, Apiluck
Spent coffee grounds (SCGs) can be used as biomass to synthesize activated carbon (AC) through physical carbonization and chemical activation. Epichlorohydrin (EP) was used to create the chitosan (CS) and AC biopolymer composites via emulsion crosslinking. The main goal of this research is to boost the efficiency of CS/AC/EP composite materials for carbon dioxide (CO<inf>2</inf>) capture by adsorption. The impact of CS content, AC concentration, and EP quantity on CO<inf>2</inf> removal was studied applying the Box–Behnken design (BBD)-based response surface methodology (RSM) and artificial neural network (ANN)-based artificial intelligence (AI) models. The conditions for the adsorption process were optimized to forecast the maximum CO<inf>2</inf> adsorption utilizing BBD and ANN approaches. Optimal process parameters of 15.11 g CS content, 38.95 %w/w AC concentration, and 7.16 g EP quantity resulted in a CO<inf>2</inf> adsorbed of approximately 7.62 cm3/g. The coefficient of determination (R2) for the BBD model was 0.9995, while the correlation coefficient (R) for the ANN model was 0.9992. The CO<inf>2</inf> adsorption efficiency of adsorbents is enhanced by increasing the amounts of AC and EP. This study provides a technique for predicting and improving CO<inf>2</inf> capture through the development of porous polymer composite beads (CBs) with a high CO<inf>2</inf> adsorption capacity.

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