Biphasic pretreatment excels over conventional sulfuric acid in pinewood biorefinery: An environmental analysis.

This study assesses the environmental impact of pine chip-based biorefinery processes, focusing on bioethanol, xylonic acid, and lignin production. A cradle-to-gate Life Cycle Assessment (LCA) is employed, comparing a novel biphasic pretreatment method (p-toluenesulfonic acid (TsOH)/pentanol, Sc-1) with conventional sulfuric acid pretreatment (H2SO4, Sc-2). The analysis spans biomass handling, pretreatment, enzymatic hydrolysis, yeast fermentation, and distillation. Sc-1 yielded an environmental impact of 1.45E+01 kPt, predominantly affecting human health (96.55%), followed by ecosystems (3.07%) and resources (0.38%). Bioethanol, xylonic acid, and lignin contributed 32.61%, 29.28%, and 38.11% to the total environmental burdens, respectively. Sc-2 resulted in an environmental burden of 1.64E+01 kPt, with a primary impact on human health (96.56%) and smaller roles for ecosystems (3.07%) and resources (0.38%). Bioethanol, xylonic acid, and lignin contributed differently at 22.59%, 12.5%, and 64.91%, respectively. Electricity generation was predominant in both scenarios, accounting for 99.05% of the environmental impact, primarily driven by its extensive usage in biomass handling and pretreatment processes. Sc-1 demonstrated a 13.05% lower environmental impact than Sc-2 due to decreased electricity consumption and increased bioethanol and xylonic acid outputs. This study highlights the pivotal role of pretreatment methods in wood-based biorefineries and underscores the urgency of sustainable alternatives like TsOH/pentanol. Additionally, adopting greener electricity generation, advanced technologies, and process optimization are crucial for reducing the environmental footprint of waste-based biorefineries while preserving valuable bioproduct production.

Comparison of shrimp waste-derived chitosan produced through conventional and microwave-assisted extraction processes: Physicochemical properties and antibacterial activity assessment.

Depending on its physicochemical properties and antibacterial activities, chitosan can have a wide range of applications in food, pharmaceutical, medicine, cosmetics, agriculture, and aquaculture. In this experimental study, chitosan was extracted from shrimp waste through conventional extraction, microwave-assisted extraction, and conventional extraction under microwave process conditions. The effects of the heating source on the physicochemical properties and antibacterial activity were investigated. The results showed that the heating process parameters affected the physicochemical properties considerably. The conventional procedure yielded high molecular weight chitosan with a 12.7 % yield, while the microwave extraction procedure yielded a porous medium molecular weight chitosan at 11.8 %. The conventional extraction under microwave process conditions led to medium molecular weight chitosan with the lowest yield (10.8 %) and crystallinity index (79 %). Antibacterial assessment findings revealed that the chitosan extracted using the conventional method had the best antibacterial activity in the agar disk diffusion assay against Listeria monocytogenes (9.48 mm), Escherichia coli. (8.79 mm), and Salmonella Typhimurium (8.57 mm). While the chitosan obtained by microwave-assisted extraction possessed the highest activity against E. coli. (8.37 mm), and Staphylococcus aureus (8.05 mm), with comparable antibacterial activity against S. Typhimurium (7.34 mm) and L. monocytogenes (6.52 mm). Moreover, the minimal inhibitory concentration and minimal bactericidal concentration assays demonstrated that among the chitosan samples investigated, the conventionally-extracted chitosan, followed by the chitosan extracted by microwave, had the best antibacterial activity against the target bacteria.

Exergetic performance evaluation of a diesel engine powered by diesel/biodiesel mixtures containing oxygenated additive ethylene glycol diacetate.

A diesel engine running on diesel/biodiesel mixtures containing ethylene glycol diacetate (EGDA) was investigated from the exergoeconomic and exergoenvironmental viewpoints. Biodiesel was mixed with petrodiesel at 5% and 20% volume ratios, and the resultant mixtures were then doped with EGDA at 1-3% volume ratios. The exergetic sustainability indicators of the engine operating on the prepared fuel formulations were determined at varying engine loads. The indicators were selected to support decision-making on fuel composition and engine load following thermodynamic, economic, and environmental considerations. The engine load markedly affected all the studied exergetic parameters. The highest engine exergetic efficiency (39.5%) was obtained for petrodiesel doped with 1 v/v% EGDA at the engine load of 50%. The minimum value of the unit cost of brake power exergy (49.6 US$/GJ) was found for straight petrodiesel at full-load conditions, while the minimum value of the unit environmental impact of brake power exergy (29.9 mPts/GJ) was observed for petrodiesel mixed with 5 v/v% biodiesel at the engine load of 75%. Overall, adding EGDA to fuel mixtures did not favorably influence the outcomes of both exergetic methods due to its energy-intensive and cost-prohibitive production process. In conclusion, although petrodiesel fuel improvers such EGDA used in the present study could properly mitigate pollutant emissions, the adverse effects of such additives on thermodynamic parameters of diesel engines, particularly on exergoeconomic and exergoenvironmental indices, need to be taken into account, and necessary optimizations should be made before their real-world application.

Soft computing-based modeling and emission control/reduction of a diesel engine fueled with carbon nanoparticle-dosed water/diesel emulsion fuel.

This study was set up to model and optimize the performance and emission characteristics of a diesel engine fueled with carbon nanoparticle-dosed water/ diesel emulsion fuel using a combination of soft computing techniques. Adaptive neuro-fuzzy inference system tuned by particle swarm algorithm was used for modeling the performance and emission parameters of the engine, while optimization of the engine operating parameters and the fuel composition was conducted via multiple-objective particle swarm algorithm. The model input variables were: injection timing (35-41° CA BTDC), engine load (0-100%), nanoparticle dosage (0-150 µM), and water content (0-3 wt%). The model output variables included: brake specific fuel consumption, brake thermal efficiency, as well as carbon monoxide, carbon dioxide, nitrogen oxides, and unburned hydrocarbons emission concentrations. The training and testing of the modeling system were performed on the basis of 60 data patterns obtained from the experimental trials. The effects of input variables on the performance and emission characteristics of the engine were thoroughly analyzed and comprehensively discussed as well. According to the experimental results, injection timing and engine load could significantly affect all the investigated performance and emission parameters. Water and nanoparticle addition to diesel could markedly affect some performance and emission parameters. The modeling system could predict the output parameters with an R2 > 0.93, MSE < 5.70 × 10-3, RMSE < 7.55 × 10-2, and MAPE < 3.86 × 10-2. The optimum conditions were: injection timing of 39° CA BTDC, engine load of 74%, nanoparticle dosage of 112 µM, and water content of 2.49 wt%. The carbon dioxide, carbon monoxide, nitrogen oxides, and unburned hydrocarbon emission concentrations were found to be 7.26 vol% , 0.46 vol% , 95.7  ppm, and 36.2 ppm, respectively, under the selected optimal operating conditions while the quantity of brake thermal efficiency was found at an acceptable level ( 34.0 %). In general, the applied soft computing combination appears to be a promising approach to model and optimize operating parameters and fuel composition of diesel engines.

Data supporting consolidating emission indices of a diesel engine powered by carbon nanoparticle-doped diesel/biodiesel emulsion fuels using life cycle assessment framework.

Integrated environmental analysis using life cycle assessment for different fuel blends used in a single-cylinder diesel engine was performed to select the most eco-friendly fuel blend. More specifically, the inventory data in support of the integrated environmental analysis of water-emulsified 5% biodiesel/diesel blends (B5) containing different levels of carbon nanoparticles (i.e., 38, 75, and 150 µM) as a novel fuel nanoadditives at a fixed engine speed of 1000 rpm and four different engine loads (i.e., 25, 50, 75, and 100%) are presented. Neat diesel, B5, and B5 containing water (3 wt.%) were used as controls. Raw data related to the production and combustion of fuel blends were experimentally collected. Industrial (i.e., experiments at large scale) and laboratory (i.e., experiments at small scale) data were used for fuel blends production while experimental data obtained by engine tests were used for the combustion stage. Then raw data were processed with the IMPACT 2002+ methods by using the SimaPro software and EcoInvent database and were then converted into environmental impacts. Accordingly, six supplementary files including the inventory data on integrated environmental analysis of the different fuel blends are presented (Supplementary Files 1-6). The data could be applied for integrated environmental analysis in order to avoid subjective weighting of combustion parameters for selecting the most eco-friendly fuel blend for use in diesel engines. More specifically, by developing a single score indicator obtained through conducting integrated combustion analysis, comparison of various fuel blends is largely facilitated.