Biomass Conversion and Biorefinery最新文献

This study investigates the use of a FeCl₃-hydrochar/drinking water treatment sludge (FeCl₃-HC/DWTS) catalyst as a catalyst for industrial wastewater treatment through the photo-Fenton process. The catalyst was synthesized by activating hydroxide sludge from a drinking water treatment plant and FeCl₃-hydrochar derived from two-phase olive mill waste via hydrothermal carbonization. The structural and chemical properties of the catalyst were characterized using XRD, SEM, EDS, ICP, and FT-IR analyses. The composite demonstrated excellent catalytic performance in the degradation of micropollutants, achieving 95% decolorization of Methyl Orange in 30 min, 91% degradation of paracetamol, and 88% degradation of salicylic acid in 200 min, using 2.5 g/L of catalyst, [H₂O₂] = 14,68 mM, and pH = 5. It maintained 88% decolorization efficiency for Methyl Orange over five successive cycles, highlighting its reusability and stability. For real industrial effluents, the catalyst achieved 98% decolorization of textile wastewater within 150 min using 4.5 g of catalyst. These results indicate that the FeCl₃-HC/DWTS catalyst can be effectively used for the treatment of industrial wastewater, contributing to more sustainable wastewater management practices by minimizing sludge waste and enhancing the degradation of recalcitrant pollutants.

Pakistan is heavily reliant on fossil fuels, which constitute a significant portion of its energy mix with 48% from natural gas, 33% from furnace oil and only 7% from indigenous renewables, impacting economic activities and environmental sustaianability. Hence, agriculture waste offers enormous potential for the production of renewable energy, but it is not yet fully utilized. In order to estimate Pakistan’s potential for energy production from 2023 to 2053, a forecast model incorporating historical trends in agricultural yield has been developed in the Low Emissions Analysis Platform (LEAP^®) software and put into practice for agricultural residue-based biomass resource assessment of six primary agricultural residue (bagasse, rice straw, wheat straw, corn stover, cotton straw and rice husk). It was discovered that Pakistan would have access to approximately 54 million tons of agricultural waste for the purpose of generating electricity in 2023, which translates to a potential capacity of almost 15 GW. By 2053, there will be 135 million tonnes of agricultural residue and renewable energy potential is expected to gradually rise up to 35 GW. Furthermore, the capital cost of implanting a power system based on agriculture residue will increase from 134.250 million US dollars in 2023 to 313.250 million US dollars till 2053. Agricultural residue represents a valuable resource that can enhance soil health, provide renewable energy, and create economic opportunities. Accordingly, this study provides sustainable and affordable forecasting of agricultural residue based clean energy potential and it is suggested to adopt the sustainable practices for recycling agricultural residues, that helps to mitigate environmental impacts while maximizing benefits for agricultural productivity and energy security.

Tequila agave leaves (TAL) are residues from the tequila industry left in the fields. They pose a serious environmental threat, as a source of stenches and pest infestations, since approximately 700,000 tons are generated annually. This study proposes the integral use of TAL to produce succinic acid, since TAL contain juice, rich in soluble carbohydrates, and fibers that can be hydrolyzed. The juice was extracted from the fibers and its chemical composition was determined. Juice, fibers and delignified fibers were subjected to different hydrolysis treatments to obtain fermentable sugars: acid (H₂SO₄ or HCl), enzymatic (Macerex^R or Celuzyme^R), and combined acid-enzymatic. The treatments that resulted in a higher sugar concentration were selected for succinic acid production with Actinobacillus succinogenes. It was found that the juice had a sugar content (glucose and fructose) of 31.3 g/L. The fibers, on the other hand, were composed primarily of cellulose and hemicellulose (70 wt%) and the highest concentration of fermentable sugars (28.4 g/L; 42% conversion efficiency) was achieved with fibers HCl-Celuzyme hydrolysis. Fermentations with A. succinogenes were carried out using the raw juice and fiber hydrolyzates (acid, enzymatic, and HCl-Celuzyme). The highest production of succinic acid was obtained with the juice (12.5 g/L succinic acid, Y_S/M = 0.48 g/g, r_S = 0.70 g/L·h) and with HCl – Celuzyme hydrolyzates (13.6 g/L succinic acid, Y_S/M = 0.57 g/g, r_S = 0.60 g/L·h). This study demonstrates that the leaves from tequila agave represent a promising renewable source to obtain substrates to produce succinic acid by A. succinogenes.

Novel cellulose derivatives containing phosphate groups (phosphate@cellulose) have been synthesized for the first time for organic dyes adsorption. Dialdehyde cellulose was synthesized via selective oxidation with sodium periodate. O-phosphorylethanolamine was introduced to the cellulose backbone through the Schiff-base formation followed by reductive amination with 2-picoline borane. TGA analysis revealed that phosphate modification significantly improved the thermal stability of cellulose. SEM confirmed the morphological transformation, while EDX analysis verified the incorporation of nitrogen and phosphorus. XPS analysis confirmed the functionalization with phosphate groups, as evidenced by the appearance of distinct N1s and P2p peaks. Methylene blue adsorption revealed that phosphate@cellulose exhibited a maximum adsorption capacity of 399 mg/g. Chemisorption is implied by the adsorption kinetics, which follow a pseudo-second-order model, and the data aligned well with the Langmuir isotherm model. Thermodynamic evaluation exposed that the adsorption is spontaneous, endothermic, and accompanied by an increase in entropy, demonstrating favorable interactions between the phosphate@cellulose and MB molecules. Significantly, phosphate@cellulose demonstrated potential for regeneration and reusability over several cycles. Given the findings, phosphate@cellulose derivative could be considered as an efficient biosorbent for the elimination of cationic dyes from industrial wastewater and compelling alternative to conventional adsorbents.

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A bio-based coagulant of Cassia sieberiana isolated extract (CSIE) was obtained as a by-product of sequential ethanol multiple salt oil extraction from Cassia sieberiana seed (CS) for the treatment of ceramic tile industry effluent (CTIE). The coagulant and sludge were characterized by proximate and elemental analyses, and scanning electron microscope/energy dispersive x-ray spectroscopy (SEM/EDX), Fourier transform infrared spectroscopy (FTIR), thermo-gravimetric analysis/differential scanning calorimetry (TGA/DSC) and X-ray diffraction (XRD). The effects of pH, dosage, settling time and temperature on coagulation and flocculation of turbidity in the CTIE were investigated. The proximate and elemental analyses of the CS precursor gave 29.91% protein, 56.38% carbohydrate, 33.15 mg/g calcium, 90.72 mg/g magnesium, and 265.02 mg/g potassium amongst other selected metals. The effectiveness of CSIE was compared to that of alum, a conventional coagulant, under similar conditions. The semi-crystalline nature of CSIE, as revealed by XRD analysis, suggests that it possesses both amorphous regions for effective adsorption and crystalline regions for structural stability during the coagulation process. The optimum coagulation of CSIE occurred at pH 8 in 30 min and 200 µg/ml coagulant dosage with removal efficiency of 96.16% at 25 °C. The observed second-order kinetics suggests that the coagulation process is dependent on the interaction between CSIE and the pollutants, indicating that the rate-limiting step involves the chemical reaction between the coagulant and the pollutants in the wastewater. This study highlights the potential of CSIE as a sustainable and effective alternative to traditional chemical coagulants for wastewater treatment.

Pharmaceutical wastewater treatment presents a significant environmental challenge due to the complex combination of organic and inorganic contaminants involved. This study explored the effectiveness of lime peel-based materials (unmodified lime peel (LP), lime peel biochar (LB), and clay-impregnated lime peel biochar (CI-LB)) for treating pharmaceutical effluent within a packed bed filtration system. The materials were analyzed using FTIR, SEM, XRF, and TGA/DTA/DSC to assess their surface properties, functional groups, elemental composition, and thermal stability. The treatment trials indicated some reductions in biological oxygen demand (BOD from 12.10 to 10.7 mg/L), chemical oxygen demand (COD from 8.14 to 8.09 mg/g), total suspended solids (TSS from 0.10 to 0.05 mg/g), and heavy metal concentrations. CI-LB proved to be the most effective filter material, achieving optimal pollutant removal with 24-hour contact time, reducing Copper, Cadmium, Nickel, Zinc, and Manganese to below-detectable levels. However, all materials showed limited success in decreasing total dissolved solids (TDS) and electrical conductivity (EC), with increases of 48.4%, 113%, and 190.6% for LB, CI-LB, and LP respectively. The study concludes that lime peel-based materials, particularly CI-LB, show significant potential for pharmaceutical effluent remediation but require further optimization to address TDS and EC challenges. Future research should investigate combined treatment strategies and material enhancements to fully address these challenges.

The objective of this study is to examine the potential of the calyx of Physalis peruviana (PPC) as a biosorbent for the removal of the highly toxic Crystal violet dye (CV) from aqueous solutions. In order to characterise the biosorbent, a series of analyses were conducted, including FTIR, SEM, EDX, and zero point of charge analyses. The compatibility of the Langmuir, Freundlich, and Dubinin-Radushkevich isotherm models was evaluated in order to assess the biosorption isotherms. The Langmuir isotherm model, which exhibited the highest correlation coefficient (R² = 0.930), was identified as the most accurate description of the biosorption process. At 25 °C and a natural pH (4.8), the monolayer biosorption capacity was determined to be 144 mg g^− 1. The biosorption kinetic mechanism was found to align with both the PSO. (R² = 0,943), and the IPD model (R² = 0,990). The thermodynamic parameters indicated that the biosorption process is heat-absorbing (ΔH°>0) and spontaneous (ΔG°<0), and results in an increase in entropy (ΔS°>0). It was thus determined that biological waste PPC can be evaluated as a cost efficient, renewable and effective biosorbent in CV removal and as an alternative to other sorbents that have been previously reported.

A significant quantity of kernels, a by-product, is being generated during litchi (Litchi chinensis) processing. The present study investigates the valorization of litchi kernel for extracting starch through conventional and ultrasound-assisted techniques. To maximize the yield, purity, and amylose content of litchi kernel starch, the process parameters, viz., solid-liquid ratio (1:1–1:3, w/v), amplitude (30–40%), and time (10–30 min) for ultrasound-assisted starch extraction were optimized using response surface methodology (RSM). Under predicted optimized parametric conditions for ultrasound-assisted extraction, maximum yield, purity, and amylose content of starch were obtained as 31.43%, 95.31%, and 21%, respectively, while those for conventional extraction technique were 23.9%, 91.32%, and 18.80%, respectively. The water (0.98 g/g) and oil absorption capacity (1.22 g/g), swelling power (12.97 g/g), and solubility (7.62%) of optimized ultrasound extracted starch were found to be superior to those (0.74 g/g, 1.12 g/g, 9.92 g/g, and 4.85%, respectively) of starch extracted by conventional technique (native starch). SEM images have confirmed cracks and rough surface of starch granules retrieved via ultrasonication. FT-IR and XRD analysis of ultrasound extracted starch revealed diminished peak intensity and A-type XRD pattern with lower relative crystallinity (27.34%) than native starch (32.21%), indicating its amorphous nature. Mean particle size of ultrasound extracted starch granules (10.40 μm) was lower than that of native starch (18.81 μm). The rapid visco analyzer and differential scanning colorimeter have shown improved pasting behavior and gelatinization temperature of litchi kernel starch extracted through ultrasonication.

Methylene blue (Mb) is a synthetic dye that is broadly used in many practical applications but also poses many risks to the health of living beings. In this study, for the first time in the literature, the green remediation potential of alcali-treated waste leaf biomass of Prunus laurocerasus L. for Mb biosorption from aqueous medium was investigated. The biosorption process was characterized through the techniques of SEM and FTIR. The biosorbent exhibited a favorable surface structure for Mb biosorption. The biosorbent reached peak efficiency at pH of 8, biosorbent dose of 10 mg, biosorption time of 120 min and initial Mb concentration of 15 mg L^− 1. For Mb biosorption, the isotherm data agreed well with Dubinin-Radushkevich (D-R) isotherm model, while the kinetic data provided a good fit to the pseudo-second-order (PSO) kinetic model. The biosorption was thermodynamically physical, spontaneous and favorable process. Mb biosorption capacity of the biosorbent was calculated to be 141.098 mg g^− 1. High success of the biosorbent in Mb biosorption indicated its superior environmental remediation potential for large-scale wastewater treatment in an environmentally responsible way.

Co-pyrolysis of biomass with plastics can significantly increase the added value of biochar. Machine learning techniques can effectively predict the yield of biochar from biomass and plastic co-pyrolysis. This study innovatively introduces data from the co-pyrolysis of the three major biomass components (cellulose, hemicellulose, and lignin) with plastics, including both individual and mixed biomass components, improving upon the limitation of traditional research that focuses solely on the overall characteristics of biomass. Based on the Light Gradient Boosting Machine (Light GBM) and Deep Neural Network (DNN) algorithm, a predictive model for biochar yield from biomass and plastic co-pyrolysis was developed. The results show that LightGBM outperforms DNN. The introduction of these data significantly improves LightGBM’s prediction accuracy and performance, with an average increase in R² of 0.07 and an average decrease in MAE and RMSE by approximately 2. LightGBM_c achieves the best performance, with an R² of 0.901, MAE of 3.672, and RMSE of 4.792. This study proposes a novel approach to improve predictive models in thermochemical conversion research involving biomass and plastics, which provides valuable insights for optimizing the preparation of biochar and increasing biochar yield.