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In the present investigation, calcium oxide solid nanocatalyst derived from the egg shell and Nannochloropsis salina were used for the production of biodiesel. The morphological characteristics and functional groups of synthesized nanocatalyst was characterized by SEM and FTIR analysis. Process variables optimization for biodiesel production was studied using RSM and ANN. The R2 values for RSM and ANN was found to be 0.8751 and 0.957 which showed that the model was significantly fit with the experimental data. The maximum FAME conversion for the synthesized nanocatalyst CaO was found to be 86.1% under optimum process conditions (nanocatalyst amount: 3% (w/v); oil to methanol ratio 1:6 (v/v); reaction temperature: 60 °C; reaction time 55 min). Concentration of FAME present in biodiesel was identified by GC–MS analysis.

Ionic liquid solvents (ILSs) have been effectively utilized in biomass pretreatment to produce cellulose-rich materials (CRMs). Predicting CRM properties and evaluating multi-dimensional relationships in this system are necessary but complicated. In this work, machine learning algorithms were applied to predict CRM properties in terms of cellulose enrichment factor (CEF) and solid recovery (SR), using 23-feature datasets from biomass characteristics, operating conditions, ILSs identities, and catalyst. Random forest algorithm was found to have the highest prediction accuracy with RMSE and R2 of 0.22 and 0.94 for CEF, as well as 0.07 and 0.84 for SR, respectively. Highly influential features on making predictions were mainly from biomass characteristics andILS treatment'soperating conditions, totally contributed 80% on CEF and 60% on SR. One- and two-way partial dependence plots were used to explain/interpret the multi-dimensional relationships of the most important features. Our findings could be applied in designing new ILSs and optimizing the process conditions.

In industrial processes, chemical catalysis is being replaced by enzyme catalysis, since the latter is environmentally benign, non-persistent and cost effective. Microbial xylanases have significant applications in textile, baking, food and feed industries, and in paper and pulp industries for reducing the chlorine requirement. The hazardous chlorine required for bleaching can be reduced up to 25-30% by including an enzymatic step in the pulp bleaching process. The paper pulp bleaching requires xylanases that are active at alkaline pH and elevated temperatures. The enzymes from the cultured microbes do not perform optimally in the paper industry due to their inadequate stability under the process conditions of high temperature and alkaline pH. This review, therefore, deals with the rationale of molecular approaches such as protein engineering for designing xylanases with improved characteristics to suit the process conditions in industries, and prospects and problems.

This research work pertained to establishment of timing relationship for two sludge composting phases proceeding in different conditions. The first phase was performed in a dynamical reactor and the second – in periodically turned windrows. The research was carried out in three independent repetitions with five test series cycles in each varying in the hot phase timing. The compost windrows ripening phase was monitored for 5 months. The determined physical and chemical indicators (independent variables) made a basis for calculation of compost ripeness indices. To perform the numerical analysis an artificial neural network performing the generalised regression task was used. Using specific values of particular indices, as the ripeness criterion, a td = f(tp) model was created, in form of decreasing linear function, interpreting the relationship between time of compost keeping in the reactor and the time of compost windrow ripening. The obtained results suggest that keeping the batch in reactor for a period in excess of 18 days is groundless, therefore, ineffective as it does not shorten significantly the compost ripening phase.

The purpose of this study is to develop regression models that describe the role of process conditions and feedstock chemical properties on carbonization product characteristics. Experimental data were collected and compiled from literature-reported carbonization studies and subsequently analyzed using two statistical approaches: multiple linear regression and regression trees. Results from these analyses indicate that both the multiple linear regression and regression tree models fit the product characteristics data well. The regression tree models provide valuable insight into parameter relationships. Relative weight analyses indicate that process conditions are more influential to the solid yields and liquid and gas-phase carbon contents, while feedstock properties are more influential on the hydrochar carbon content, energy content, and the normalized carbon content of the solid.

In this study, different from previous reports, an alternative process for detoxification of Cr(VI)-contaminated water with polypyrrole-engineered pulp fibers prepared using low cost hydrogen peroxide as oxidant was proposed. The process conditions in preparation of the engineered fibers as well as the water treatment conditions were optimized, and the behavior of Cr desorption from the engineered fibers was evaluated. The results showed that the proposed process was highly efficient in Cr(VI)-detoxification via the integration of adsorption with reduction. Compared with the previously reported polyaniline-engineered fibers prepared with hydrogen peroxide as oxidant, the engineered fibers studied in this work was much more effective.

There is little research literature on the conversion of lignocellulosic rich waste streams to ethanol, and even fewer have investigated both the technical aspects and environmental impacts together. This study assessed technical and environmental challenges of converting three lignocellulosic waste streams to ethanol: municipal solid waste (MSW), low grade mixed waste paper (MWP), and organic yard waste (YW). Experimental results showed high conversion yields for all three streams using suitable conversion methods. Environmental impacts are highly dependent on conversion technology, and process conditions used. Life cycle assessment results showed that both chemicals production and waste collection are important factors to be included within a waste-to-ethanol study.

A novel Tubificidae-reactor with special porous carrier and combined aeration system was designed to investigate the sludge reduction. The influences of change in frequency of high-intensity aeration (FHIA), dissolved oxygen (DO) content, initial sludge concentration (ISC) and sludge retention time (SRT) on the immobilization of Tubificidae and sludge reduction were evaluated. Furthermore, response surface method was applied to optimize the process conditions using the sludge reduction rate and stable immobilization of worms as the target parameters to optimize, and DO as the control factors to be optimized with the fluctuation of influent ISC, while keeping FHIA and SRT at optimum level. Attractive sludge reduction rate (470 mg L(-1)d(-1)) can be obtained using the strategies indicated by the highly correlated model. The optimum conditions were found to be DO of 1.0-1.6 mg/L and ISC of 3000-4000 mg/L respectively, while keeping FHIA at 12 times/d and SRT at 2d.