Bao The Nguyen

By Bao The Nguyen

This chapter presents a numerical model to estimate the performance of solar basin-type distillation systems, both for conventional passive solar stills and active (forced circulation) stills with enhanced heat recovery. It also analyzes the factors affecting the distillate outputs of the still, including environmental factors (external factors or natural), elements of the design and operation (subjective factors). The subjective elements as well as the measures taken to optimize these factors are thoroughly analyzed. With these measures, the distillate yields of solar stills are increased from 30 to 68% compared with traditional distillation systems. This has scientific significance and practicality enabling the application of this technology to solar water distillation using a source of clean and renewable energy. It provides a viable way to alleviating the problem of the availability of clean water, especially in those areas and communities in countries where water resources are increasingly polluted and salty.

Part of the book: Desalination and Water Treatment

By Nguyen The Bao

This chapter presents a numerical model for calculating basin-type solar water distillation. The model is used to calculate solar distillation for both passive natural convection and forced convection with external condensers. For passive systems, the numerical model allows to simulate and calculate more complex parameters than previous models. For active-forced convection systems, this model allows the simulations of the heat transfer and mass process inside both the distillation unit and the internal heat exchanger. Comparison of numerical simulation results and experimental results shows that the numerical model achieves the acceptable accuracy in calculating the parameters of the fluid flow inside the distillation and the condenser-type heat recovery as well as estimation of the distillate output corresponding to both types of solar distillation.

Part of the book: Distillation

By Bao The Nguyen

According to the natural geographical distribution, developing countries are concentrated in tropical climates, where radiation is abundant. So the use of solar energy is a sustainable solution for developing countries. However, daily or hourly measured solar irradiance data for designing or running simulations for solar systems in these countries is not always available. Therefore, this chapter presents a model to calculate the daily and hourly radiation data from the monthly average daily radiation. First, the chapter describes the application of Aguiar’s model to the calculation of daily radiation from average daily radiation data. Next, the chapter presents an improved Graham model to generate hourly radiation data series from monthly radiation. The above two models were used to generate daily and hourly radiation data series for Ho Chi Minh City and Da Nang, two cities representing two different tropical climates. The generated data series are tested by comparing the statistical parameters with the measured data series. Statistical comparison results show that the generated data series have acceptable statistical accuracy. After that, the generated radiation data series continue to be used to run the simulation program to calculate the solar water distillation system and compare the simulation results with the radiation data. Measuring radiation. The comparison results once again confirm the accuracy of the solar irradiance data generation model in this study. Especially, the model to generate the sequences of hourly solar radiation values proposed in this study is much simpler in comparison to the original model of Graham. In addition, a model to generate hourly ambient tempearure date from monthly average daily ambient temperature is also presented and tested. Then, both generated hourly solar radiation and ambient temperature sequences are used to run a solar dsitillation simulation program to give the outputs as monthly average daily distillate productivities. Finally, the outputs of the simulation program running with the generated solar radiation and ambient temperature data are compared with those running with measured data. The errors of predicted monthly average daily distillate productivities between measured and generated weather data for all cases are acceptably low. Therefore, it can be concluded that the model to generate artificial weather data sequences in this study can be used to run any solar distillation simulation programs with acceptable accuracy.

Part of the book: Distillation Processes