Distillation Operations

Modeling Passive Solar Distillation Production in Las Vegas, NV

by Noe Santos

Final thesis

A study has been performed to examine the effects of daily weather on the performance of commercial solar distillation... more A study has been performed to examine the effects of daily weather on the performance of commercial solar distillation basins (solar stills). The objectives of this study were to evaluate the long term performance of solar stills, to instrument two solar stills and record sub-hourly thermal properties, to evaluate existing heat transfer modeling methods for hourly production, and to create new models to predict daily production using experimental distillate production and local weather data by utilizing artificial neural networks, genetic algorithms, and multivariate regression. A system dynamics model was also created to determine the required basin area and storage volume to produce enough water to meet year round potable water demand.

Solar still production was measured between January 2011 and September 2011. The average daily yield of solar still #1-A (SS1-A) and solar still #1-B (SS1-B) ranged from 2.11 ± 0.35 L/m2 and 2.00 ± 0.46 L/m2 (winter season) to 5.53 ± 1.01 L/m2 and 5.64 ± 1.06 L/m2 (summer season), respectively.

The artificial neural network model performed with a mean absolute error as low as 9.4% with up to 92.4% of production predictions within 0-20% of the actual daily production. The genetic algorithm model performed with a mean absolute error as low as 11% with up to 91% of production predictions within 0-20% of the actual daily production. The multivariate regression model performed with a mean absolute error as low as 9.7% with up to 94.1% of production predictions within 0-20% of the actual daily production.

Analysis of the sub-hourly performance data indicated that large distilland volumes resulted in a greater proportion of production occurring during the night compared to smaller distilland volumes. Hourly temperature data was used to calculate heat transfer coefficients which could predict hourly distillate production with a mean absolute error between 26% and 53%.

Numerical Simulation of the Direct Application of Compound Parabolic Concentrators to a Single Effect Basin Solar Still

by Joshua Pearce

Joshua M. Pearce and David C. Denkenberger, "Numerical Simulation of the Direct Application of Compound Parabolic Concentrators to a Single Effect Basin Solar Still", Proceedings of the 2006 International Conference of Solar Cooking and Food Processing, p. 118, 2006.

As regional shortages of fresh water become more prevalent, solar distillation using a single-effect basin holds... more As regional shortages of fresh water become more prevalent, solar distillation using a single-effect basin holds promise as a method to bring low-cost, clean, and ecologically-responsible water to remote area dwellers. Compound parabolic concentrators (CPCs) can be used to direct more light onto the still increasing the throughput and efficiency of these passive solar devices. A computer program has been developed that uses the properties of materials and the solar energy characteristics of the site to calculate the increase in output of water due to reflectors of different height. For reflector 2.5 times the width of the still, the output per unit area per day roughly triples with only ~10% increase in cost and moderate maintenance (weekly tilts), indicating that CPCs have a significant economic advantage in producing solar distilled water.

Ein brennendes Thema: Der Destillierhelmfund in der ehemaligen Badestube von Zwettl-Niederösterreich und die Rolle der Destillation im Mittelalter und in der Frühen Neuzeit

by Ronald Salzer

published in "Medium Aevum Quotidianum" 61, 2010, 27-55.

Modeling solar still production using local weather data and artificial neural networks

by Noe Santos

A study has been performed to predict solar still distillate production from single examples of two different... more A study has been performed to predict solar still distillate production from single examples of two different commercial solar stills that were operated for a year and a half. The purpose of this study was to determine the effectiveness of modeling solar still distillate production using artificial neural networks (ANNs) and local weather data. The study used the principal weather variables affecting solar still performance, which are the daily total insolation, daily average wind velocity, daily average cloud cover, daily average wind direction and daily average ambient temperature. The objectives of the study were to assess the sensitivity of the ANN predictions to different combinations of input parameters as well as to determine the minimum amount of inputs necessary to accurately model solar still performance. It was found that 31e78% of ANN model predictions were within 10% of the actual yield depending on the input variables that were selected. By using the coefficient of determination, it was found that 93e97% of the variance was accounted for by the ANN model. About one half to two thirds of the available long term input data were needed to have at least 60% of the model predictions fall within 10% of the actual yield. Satisfactory results for two different solar stills suggest that, with sufficient input data, the ANN method could be extended to predict the performance of other solar still designs in different climate regimes.

Comparing multivariate regression and artificial neural networks to model solar still production

by Noe Santos

Presented at the 2011 American Solar Energy Society conference in Raleigh, NC

Distillation column tray hydraulics

by SUBHASISH MITRA

Selection of distillation trays dictates performance of intended separation process. This presentation aims to... more Selection of distillation trays dictates performance of intended separation process. This presentation aims to providing basic fundamentals of distillation tray design aspects and selection criteria.