Utilization of polymers as biomaterials has greatly impacted the advancement of modern medicine. Specifically, polymeric biomaterials that are biodegradable provide the significant advantage of being able to be broken down and removed after they have served their function. Applications are wide ranging with degradable polymers being used clinically as surgical sutures and implants. To fit functional demand, materials with desired physical, chemical, biological, biomechanical, and degradation properties must be selected. Fortunately, a wide range of natural and synthetic degradable polymers has been investigated for biomedical applications with novel materials constantly being developed to meet new challenges. This review summarizes the most recent advances in the field over the past 4 years, specifically highlighting new and interesting discoveries in tissue engineering and drug delivery applications.

Biomedical applications of biodegradable polymers

https://repositorio.lneg.pt/bitstream/10400.9/2873/1/Fuel_Vol.153_128.pdf

Third generation biohydrogen production by Clostridium butyricum and adapted mixed cultures from Scenedesmus obliquus microalga biomass

In view of the looming energy crisis as a result of depleting fossil fuel resources and environmental concerns from greenhouse gas emissions, the need for sustainable energy sources has secured global attention. Research is currently focused towards renewable sources of energy due to their availability and environmental friendliness. Biofuel production like other bioprocesses is controlled by several process parameters including pH, temperature and substrate concentration; however, the improvement of biofuel production requires a robust process model that accurately relates the effect of input variables to the process output. Artificial neural networks (ANNs) have emerged as a tool for modelling complex, non-linear processes. ANNs are applied in the prediction of various processes; they are useful for virtual experimentations and can potentially enhance bioprocess research and development. In this study, recent findings on the application of ANN for the modelling and optimization of biohydrogen, b...

https://www.tandfonline.com/doi/pdf/10.1080/13102818.2016.1269616

Artificial neural networks: an efficient tool for modelling and optimization of biofuel production (a mini review)

Multi-response optimization of process parameters in biogas production from food waste using Taguchi – Grey relational analysis

This paper presents the results of laboratory experiments carried out on an anaerobic digestion of food waste with respect to the effect of solid concentration and pH. Total solid (TS) content ranging from 5.0% to 15.0% and pH value ranging from 5 to 9 were analyzed. Laboratory scale experiments using 2 l bioreactors were performed in batch mode operated at mesophilic temperature condition with a hydraulic retention time of 30 days. The cumulative volume of biogas produced was used to measure the reactor performance. The methane and carbon dioxide composition of the gas produced was measured using infra-red gas analyzers. The kinetics of biogas produced has been predicted using three different models (Gompertz model, modified Gompertz model, and Logistic model). The experimental results showed that the solid concentration 7.5% of TS and 7 pH produced a maximum biogas yield. The kinetic study showed that modified Gompertz model produced perfect goodness of fit and root mean square error when compared to other two models.

Experimental and kinetic study on anaerobic digestion of food waste: The effect of total solids and pH

Optimization studies on fermentative hydrogen production were investigated using a facultative bacteria namely, Enterobacter species (MTCC 7104). The present study emphasizes the application of mathematical tools such as response surface methodology (RSM) and artificial neural network (ANN) to predict the maximum yield of hydrogen from the optimized carbon and nitrogen source. The key components such as glucose, initial pH, xylose, tryptone, yeast extract, sucrose, and peptone were screened using the Plackett-Burman design. Furthermore, rotatable central composite design and analysis of variance were adopted to investigate the interactive effect of the significant variables (xylose concentration, initial pH, and peptone concentration). Maximum experimental hydrogen yield of 1.94 mol H2/mol xylose was achieved at the optimal points predicted by the RSM. Modeling ability of ANN and RSM has also been evaluated on predicting the maximum hydrogen yield with the estimated values of root mean square error (RMSE), multiple correlation coefficients (R2), and standard error of prediction (SEP). The estimated values of RMSE, R2, and SEP for ANN model and RSM model confirm that fitness and prediction accuracy of ANN model were higher when compared to RSM model. Energy conversion efficiency and energy recovery analysis were performed for hydrogen production process using xylose as the source material.

Optimization of biohydrogen production by Enterobacter species using artificial neural network and response surface methodology

This work presents the preparation of membranes using polycaprolactone (PCL), a biodegradable polymer incorporating unmodified and modified nanoclay using phase inversion technique. The membranes prepared with the incorporation of 0.1 g to 0.4 g of unmodified nanoclay and 0.05 g to 0.15 g of modified nanoclay were studied with respect to porosity, hydrophilicity, chemistry, mechanical and thermal properties and morphological characteristics. The results showed that the membrane incorporated with unmodified nanoclay has enhanced strength and porosity compared to the membrane with modified nanoclay and neat PCL. An increase in water permeability is shown by the incorporation of unmodified nanoclay and contact angle measurement results revealed an increased hydrophilicity with a decrease in membrane roughness which was confirmed using AFM analysis. The surface morphology was examined using SEM analysis and the results revealed that on adding unmodified nanoclay, the average pore size decreased from 6.2 μm to 4.5 μm and for membranes with modified nanoclay, the average pore size was found to be 1 μm. The prepared membranes were tested for the rejection of Congo Red and the performance were analysed.

Effect of unmodified and modified montmorillonite on the properties of PCL based ultrafiltration membrane for water treatment applications

Process parameters optimization using response surface methodology (RSM) and artificial neural network (ANN) coupled with Genetic Algorithm (GA) were carried out to analyze the influence of process parameters on maximum hydrogen production and H2 yield using sucrose as a sole carbon source. Alkali pretreated mixed culture obtained from the food waste was used for fermentative hydrogen production. Box-Behnken design was applied to examine the interactive effect of the significant variables (sucrose concentration, initial pH, inoculum size, and peptone concentration). Characterization of culture indicated that the culture was gram-negative facultative anaerobe. Maximum experimental hydrogen yield of 2.36 mol H2/mol sucrose was achieved at the optimal points predicted by the RSM. Modified Gompertz model and logistic model adequately fitted well and described the fermentative hydrogen production and bacterial growth, respectively. Process modeling abilities of ANN and RSM were compared on the basis of parameters such as estimated values of root mean square error (RMSE), multiple correlation coefficients (R2), and standard error of prediction (SEP). GA couple with ANN was used to find the global optimum point and the maximum H2 yield of 2.39 mol H2/mol sucrose was found at sucrose concentration 15.5 g/l, pH 8, inoculum size (% v/v) 8.4, and peptone concentration 4.9 g/l. The estimated values of RMSE, R2, and SEP for ANN model and RSM model confirm that fitness and prediction accuracy of ANN model was higher when compared to RSM model. From this study, we confirmed that genetic algorithm coupled with ANN technique can be a powerful tool to obtain global optimization in biochemical systems.

Biohydrogen production using anaerobic mixed bacteria: Process parameters optimization studies

Synthesis of methanol blocking PVA-TiO2 cation exchange membrane for direct methanol alkaline fuel cell

The biodegradability of polycaprolactone (PCL) and the anti-fouling property of TiO2 are utilized for the preparation of membranes for water treatment. Polyethylene glycol (PEG) is added to the polymeric solution to increase the pore formation in the membranes. The composition of PEG and TiO2 nanoparticles was optimized using the central composite design and response surface methodology. Quadratic models were developed for the responses porosity and contact angle, and the optimum compositions obtained for PEG and TiO2 were 6.24 and 1 wt%, respectively. The competency of the membrane for water treatment is studied using different characterization techniques such as TG analysis, FTIR to determine the chemical interactions, UTM to examine the mechanical strength, AFM for membrane roughness determination and morphological analysis using FESEM. A twofold increase in the overall porosity and a twentyfold reduction (approx.) in pore size were observed for the composite membrane from FESEM images. The anti-fouling ability of TiO2 resulted in a flux recovery of about 90% for the composite membrane compared to the PCL–PEG membrane after bovine serum albumin filtration. Also, the presence of TiO2 increased the reversible fouling component, which can be easily removed by cleaning/backwashing. Thus, the PCL–TiO2 combination would be a better alternative for the membranes used in water treatment applications.

Shiny Joseph

Papers

Optimization of biohydrogen production by Enterobacter species using artificial neural network and response surface methodology

Effect of unmodified and modified montmorillonite on the properties of PCL based ultrafiltration membrane for water treatment applications

Biohydrogen production using anaerobic mixed bacteria: Process parameters optimization studies

Synthesis of methanol blocking PVA-TiO2 cation exchange membrane for direct methanol alkaline fuel cell

Optimization of Process Parameters using Response Surface Methodology for PCL based Biodegradable Composite Membrane for Water Purification