Fuzzy set implementation for controlling and evaluation of factors affecting multiple-effect distillers

Multiple-effect (ME) distillation was the first process used to desalt a significant amount of seawater. This process takes place in a series of effects (stages) and uses the principle of reducing the ambient pressure in the various stages in order of their arrangement. In this work, a fuzzy logic is used to evaluate the factors affecting the ME distillers. The fuzzy logic detection was performed to assess three rules; i.e., “Increase”, “Decrease”, or “No Change” in distillation system in Jordan. We considered the factors that affect the detection of yield. There are many factors affect the ME distillers include: top brine temperature (TBT), concentration factor (CF), seawater temperature (TSW), seawater pH (pHSW), seawater salinity (SSW), scale formation (SF), and CO2 release. The various characteristics for the case study was synthesized and converted into relative weights w.r.t. fuzzy set method. The fuzzy set analysis for the case study reveals increase as confirmed by the experimental data. The application of the fuzzy set methodology offers reasonable prediction and assessment for detecting yield in distillation system in Jordan.

One of the advantages of the fuzzy logic decision maker for ME distiller production (FLDMMED) method presented here is that it used minimum and maximum operations, which are easier and faster than that are used by other methods. Also FLDMMED method used fuzzy sets that enabled us to condense large amount of data into smaller set of variable rules. The detection of yield of the ME distillation systems are virtual importance. This research proposed a methodology for the detection of yield of the ME distillation systems in Jordan. The fuzzy sets enabled us to condense large amount of data, collected to detect yield in the ME distillation systems in Jordan, into a smaller set of variable rules. The various characteristics for the case study was synthesized and converted into relative weights w.r.t. fuzzy set method. Table 5 illustrates the basic readings and calculations for detecting yield. Based on Table 5, detecting yield was estimated to be 60%. Based on our proposed methodology it was revealed that there is close correlation between the actual and predicted values of detecting yield. The actual and predicted values for the case study were 65 and 60% respectively. The fuzzy set analysis for the case study reveal increase as confirmed by the experimental data in Table 5. The application of the fuzzy set methodology offers reasonable prediction and assessment for detecting yield in ME distillation system in Jordan.