Fresh water is essential for life. More and more countries around the world are facing scarcity of drinking water, which affects over 50% of the global population. Due to human activity such as industrial development and the increasing greenhouse effect, the amount of drinking water is drastically decreasing. To address this issue, various methods of sea and brackish water desalination are used. In this study, an energy analysis (specific energy consumption, SEC) of two laboratory membrane processes, reverse osmosis (RO) and pervaporation (PV), was conducted. A model feed system saline water at 0.8, and 3.5% wt. NaCl was used. The efficiency and selectivity of membranes used in PV and RO were examined, and power of the devices was measured. The desalination processes were found to have a high retention factor (over 99%) for both PV and RO. For PV, the permeate fluxes were small but they increased with increasing feed flow rate, process temperature and salt content in the feed. The calculated SEC values for both laboratory processes ranged from 2 to 70 MWh/m ³. Lowering the process temperature, which consumes 30 to 60% of the total energy used in the PV process, can be an important factor in reducing energy consumption.

Generally, Least Squares (LS) Method treats only random errors of observation vector in adjustment function models. However, both observation vector and elements of coefficient matrix of adjustment function model contain random errors. Therefore, there is no guarantee that the result of adjustment by LS method is the global optimal solution. Total Least Square (TLS) method is a primary estimation method that treats random errors of observation vector and coefficient matrix in adjustment functional models. Since TLS method take into account both random errors of observation vector and coefficient matrix based on errors-in-variables model, it is possible to improve the accuracy compared with the result of LS method. So TLS method has been applied to different fields of science and technology including signal and image processing, computer vision,communication engineering and geodesy. However, weighted total least square (WTLS) method has been not applied in geodetic network adjustment problem compared with other fields widely. So the purpose of this paper is to summarize the algorithm of WTLS briefly and to propose an application method in adjustment of triangulation network. Key problem in application of WTLS to adjustment of geodetic network is to determine the weight matrix (or cofactor matrix) for elements of coefficient matrix in adjustment function model. In this paper proposed a method to determine cofactor matrix for errors of coefficient matrix in triangulation network, and verifies the effectiveness of suggested method through example applied to triangulation network.