Pervaporation Performance of Ag-PVA Nanocomposite Membranes: Effect of Operating Temperature

The features of pervaporation are continuously improved with the production of more and more efficient membranes. In our present study, silver nanoparticles are in-situ generated in a poly (vinyl alcohol) using solution-casting in order to enhance its capability for pervaporation. The membrane is tested on the case study of ethanol dehydration by pervaporation. Effect of silver content on the pervaporation separation index and the enrichment factor of the membrane at 15 % mass water at 40 °C are reported. Pervaporation data for nanocomposite membranes show around 100 % increase in the water permeance values while the intrinsic selectivity decreases that is typical for pervaporation membranes. The water permeances of original crosslinked PVA membrane and the 2.5 % silver loaded PVA membrane are 26.65 and 70.45 (g/m2.kPa.h), respectively. The values of total flux are closely related to water flux, showing that membranes could be successfully assigned to separate water from ethanol even at the azeotropic point. The influence of temperature on the efficiency of the pervaporation process, permeation parameter and diffusion coefficient of the feed component is also discussed. The negative heat of sorption (∆Hs) values calculated on the basis of the estimated Arrhenius activation energy values indicates that the sorption process is controlled by Langmuir's mode. Our results show that the 0.5 mass% silver loaded poly (vinyl alcohol) membrane exhibits excellent PV performance

Effect of Fed-batch Culturing on the Growth and Lipid Production of Chlorella vulgaris fo. tertia Applying pH-auxostat Acetic Acid and Predefined Exponential Glucose Feeding

The techniques of heterotrophic microalgae cultivation used to be resulting in higher productivity and better yield than autotrophic culturing. Batch cultivation strategy is commonly used with high glucose concentration, but its potential is limited for biomass production at an industrial scale. Usually, the best productivity can obtain at lower glucose concentration. Moreover, other carbon sources can cause inhibition at higher concentrations. Therefore, the fed-batch cultivation strategy is an obvious choice, as it can maintain the optimal amount of carbon source can be maintained throughout the fermentation by automating the feeding. Such self-regulatory automation is provided by the pH-auxostat addition of acetic acid, which was investigated in this study for Chlorella vulgaris fo. tertia. The pH-auxostat fermentation was upscaled, then the feeding profile was modelled and transformed to another fermentation where glucose was used as a carbon source instead of acetic acid. Thus, the preferred carbon sources were compared under the same circumstances. It was found that the tested strain consumes dissolved oxygen very fast on both carbon substrates. It favored the acetic acid at high nitrogen and phosphorus concentrations. The final biomass concentration was 29.2 g/L under pH-auxostat fed-batch strategy with acetic acid and 18.8 g/L with glucose, respectively. The highest lipid content (393 mg/g) was measured from the biomass in the case of acetic acid. The fermentation settings need further optimization, but the results concluded that pH-auxostat acetic acid feeding has a great potential for scale-up of Chlorella fermentation

Modelling of Hybrid Method for VOC Removal from Process Wastewater: Distillation and Hydrophilic Pervaporation

The study is motivated by the industrial problem from pharmaceutical industry, which is ethanol and methanol removal from process wastewater. To complete this goal hybrid method is investigated and optimized. Two distillation columns are sufficient for separation of alcohol-water mixture. Suitable water can be purified as bottom product of first column. Ethanol and methanol purification is achieved with combination of second distillation column and pervaporation. The target of this research is to rigorously model and optimize the separation of water-ethanol-methanol ternary mixture in professional flowsheet simulator environment. The minimal sufficient membrane transfers area and number of minimal theoretical stages of the columns are determined. Cost estimation is also investigated according to Douglas methodology. Considering the simulation and economic results it can be determined that, the hybrid configuration is suitable for separation of ternary mixture in 99.5 weight percent purity

Sustainability assessment of biomethanol production via hydrothermal gasification supported by artificial neural network

Global warming and climate change urge the deployment of close carbon-neutral technologies via the synthesis of low-carbon emission fuels and materials. An efficient intermediate product of such technologies is the biomethanol produced from biomass. Microalgae based technologies offer scalable solutions for the biofixation of CO2, where the produced biomass can be transformed into value-added fuel gas mixtures by applying thermochemical processes. In this study, the environmental and economic performances of biomethanol production are examined using artificial neural networks (ANNs) for the modelling of catalytic and noncatalytic hydrothermal gasification (HTG). Levenberg-Marquardt and Bayesian Regularisation algorithms are applied to describe the thermocatalytic transformation involving various types of feedstocks (biomass and wastes) in the training process. The relationship between the elemental composition of the feedstock, HTG reaction conditions (380 ?C & ndash;717 ?C, 22.5 MPa & ndash;34.4 MPa, 1 & ndash;30 wt% biomass-to-water ratio, 0.3 min & ndash;60.0 min residence time, up to 5.5 wt% NaOH catalyst load) and fuel gas yield & composition are determined for Chlorella vulgaris strain. The ideal ANN topology is characterised by high training performance (MSE = 5.680E-01) and accuracies (R-2 >= 0.965) using 2 hidden layers with 17-17 neurons. The process flowsheeting of biomass-to-methanol valorisation is performed using ASPEN Plus software involving the ANN-based HTG fuel gas profiles. Cradle-to-gate life cycle assessment (LCA) is carried out to evaluate the climate change potential of biomethanol production alternatives. It is obtained that high greenhouse gas (GHG) emission reduction (-725 kg CO2,eq (t CH3OH)-1) can be achieved by enriching the HTG syngas composition with H2 using variable renewable electricity sources. The utilisation of hydrothermal gasification for the synthesis of biomethanol is found to be a favourable process alternative due to the (i) variable synthesis gas composition, (ii) heat integration, and (iii) GHG emission mitigation possibilities

Effects of Energy Intensification of Pressure-Swing Distillation on Energy Consumption and Controllability

The aim of process integration is the efficient use of energy and natural resources. However, process integration can result in a more precise process operation, that is, it influences controllability. Pressureswing distillation processes are designed for the separation of azeotropic mixtures, but their inherent heat integration option can be utilized to significantly reduce their energy consumption. One maximum-boiling and three minimum-boiling azeotropes are considered to study and compare the nonintegrated and integrated alternatives with the tool of mathematical modeling where ASPEN Plus and MATLAB software are used. The results show that the heat-integrated alternatives result in 32−45% energy savings that are proportional to the emission reduction and the consumption of natural resources. As far as the operability is concerned, the heat-integrated alternatives show worse controllability features than the nonintegrated base case. This can be due to the loss of one controllability degree of freedom. This recommends using more sophisticated control structures for the sake of safe operation if process integration is applied