Membrane distillation is a thermally-driven membrane- separation process. With this process, only water vapor passes through the microporous hydrophobic membranes. Current membrane distillation technologies are prone to membrane fouling and scaling. This can reduce production rate, cause a system shutdown for cleaning and may increase operational costs.
This technology uses soft sensing or virtual sensing, which requires fewer sensors to estimate the condition of the membrane and the spatial temperature distribution. It also supports decision making for membrane cleaning and reduces operational costs. This is achieved through advanced soft sensing algorithms, which rely on a physical model of the process and readily available outlet temperatures. The method estimates the temperature inside the module, which can be used to find optimal operating conditions. This, in turn, reduces the manufacturing cost.
The technology consists of software that combines a mathematical model with readily available real-time measurements. It analyzes outlet temperatures and production rates to estimate the membrane surface temperature and characteristics. The deviation of the estimated membrane characteristics from their nominal values and changes in the temperature values at the membrane surface are analyzed to provide a decision about the status of the membrane.
This technology provides a solution for the predictive maintenance and operation of the membranes to minimize the interruption of water production plants, which commonly happens in conventional water desalination plant, due to off-line membrane cleaning caused by the fouling. The technology can also be applied to any membrane based desalination system with further modification. It allows a real time membrane monitoring system.
This technology offers numerous benefits such as: