Simulation of Leaching of Starch from Potato in a Batch Extractor

Abstrac

Investigation of Iron and Manganese Removal from Water Sources by Tea Leaves and Rice Straw

High levels of iron and manganese in drinking water cause sediment, turbidity, bad taste and color. As there is a wide area of rice and tea under cultivation in Guilan province with an inevitable production of waste from them, it is possible to use it for removal of undesirable elements. The present study was discontinuously performed on a laboratory scale. The impact of important factors such as pH, contact time, initial concentration, adsorbent dosage and temperature were investigated. Also, using group method of data handling, the adsorption process of the batch method was modeled. The results showed that the highest iron adsorption capacity (in terms of mg/g) for tea leaves and rice straw were 19.44 and 19.99, respectively. Considering manganese, it was 19.86 for tea leaves and 19.49 for rice straw. The best conditions for removing iron and manganese from aqueous solution are at neutral pH, contact time 40-50 minutes, absorbent dose 0.05 g and temperature 25-35 °C for tea leaves and rice straw. Overall, the GMDH model performs better in predicting the final concentrations of iron and manganese in water sources. In general, it can be concluded that rice straw and tea leaves can be used as inexpensive and environmentally friendly natural absorbers in the removal of iron and manganese from water sources

Prediction of hydrate formation conditions to separate carbon dioxide from fuel gas mixture in the presence of various promoters

In this study estimation of hydrate formation conditions to separate carbon dioxide (CO2) from fuel gas mixture (CO2+H2) was investigated in the presence of promoters such as tetra-n-butylammonium bromide (TBAB), tetra-n-butylammonium fluoride (TBAF), and tetra-n-butyl ammonium nitrate (TBANO3). The emission of CO2 from the combustion of fuels has been considered as the dominant contributor to global warming and environmental problems. Separation of CO2 from fuel gas can be an effective factor to prevent many of environmental impacts. Gas hydrate process is a novel method to separate and storage some gasses. In this communication, a feed-forward artificial neural network algorithm has been developed. To develop this algorithm, the experimental data reported in the literature for hydrate formation conditions in the fuel gas system with different concentrations of promoters in aqueous phase have been used. Finally, experimental data compared with estimated data and with calculation of efficiency coefficient, mean squared error, and mean absolute error show that the experimental data and predicted data are in acceptable agreement which demonstrate the reliability of this algorithm as a predictive tool

Modelling of CO2 capture and separation from different gas mixtures using semiclathrate hydrates

In this work we present a model for predicting hydrate formation condition to separate carbon dioxide (CO2) from different gas mixtures such as fuel gas (H2+CO2), flue gas (N2+CO2), and biogas gas (CH4+CO2) in the presence of different promoters such as tetra-n-butylammonium bromide (TBAB), tetra-n-butylammonium chloride (TBAC), tetra-n-butylammonium fluoride (TBAF), tetra-n-butyl ammonium nitrate (TBANO3), and tetra-n-butylphosphonium bromide (TBPB). The proposed method was optimized by genetic algorithm. In the proposed model, hydrate formation pressure is a function of temperature and a new variable in term of Z, which used to cover different concentrations of studied systems. The study shows experimental data and predicted values are in acceptable agreement

Reliable Tools to Forecast Sludge Settling Behavior: Empirical Modeling

In water- and wastewater-treatment processes, knowledge of sludge settlement behavior is a key requirement for proper design of a continuous clarifier or thickener. One of the most robust and practical tests to acquire information about rate of sedimentation is through execution of batch settling tests. In lieu of conducting a series of settling tests for various initial concentrations, it is promising and advantageous to develop simple predictive models to estimate the sludge settlement behavior for a wide range of operating conditions. These predictive mathematical model(s) also enhance the accuracy of outputs by eliminating measurement errors originated from graphical methods and visual observations. In the present study, two empirical models were proposed based on Vandermonde matrix (VM) characteristics as well as a Levenberg–Marquardt (LM) algorithm to predict temporal height of the supernatant–sludge interface. The novelty of our modeling approach is twofold: the proposed models in this study are more robust and simpler compared to other models in the literature, and the initial sludge concentration was considered as a key independent variable in addition to the more-customarily used settling time. The prediction performance of the VM-based model was better than the LM-based model considering the statistical parameters associated with the fitting of the experimental data including coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). The values of R2, RMSE, and MAPE for the VM- and LM-based models were obtained at 0.997, 0.132, and 5.413% as well as 0.969, 0.107, and 6.433%, respectively. The proposed predictive models will be useful for determination of the sedimentation behavior at pilot- or industrial-scale applications of water treatment, when the experimental methods are not feasible, time is limited, or adequate laboratory infrastructure is not available