Using simple and easy water quality parameters to predict trihalomethane occurrence in tap water.

Monitoring of disinfection by-products (DBPs) in water supply system is important to ensure safety of drinking water. Yet it is a laborious job. Developing predictive DBPs models using simple and easy parameters is a promising way. Yet current models could not be well applied into practice because of the improper dataset (e.g. not from real tap water) they used or involving the parameters that are difficult to measure or require expensive instruments. In this study, four simple and easy water quality parameters (temperature, pH, UVA254 and Cl2) were used to predict trihalomethane (THMs) occurrence in tap water. Linear/log linear regression models (LRM) and radial basis function artificial neural network (RBF ANN) were adopted to develop the THMs models. 64 observations from tap water samples were used to develop and test models. Results showed that only one or two parameters entered LRMs, and their prediction ability was very limited (testing datasets: N25 = 46-69%, rp = 0.334-0.459). Different from LRM, the prediction accuracy of RBF ANNs developed with pH, temperature, UVA254 and Cl2 can be improved continuously by tweaking the maximum number of neuron (MN) and Gaussian function spread (S) until it reached best. The optimum RBF ANNs of T-THMs, TCM and BDCM were obtained when setting MN = 20, S = 100, 100.1 and 60, respectively, where the N25 and rp values for testing datasets reached 85-92% and 0.813-0.886, respectively. Accurate predictions of THMs by RBF ANNs with these four simple and easy parameters paved an economic and convenient way for THMs monitoring in real water supply system.

Analysis of the convergence rules of full-range PSD surface error of magnetorheological figuring KDP crystal.

A new non-aqueous and abrasive-free magnetorheological finishing (MRF) method is adopted for processing potassium dihydrogen phosphate (KDP) crystal due to its low hardness, high brittleness, temperature sensitivity, and water solubility. This paper researches the convergence rules of the surface error of an initial single-point diamond turning (SPDT)-finished KDP crystal after MRF polishing. Currently, the SPDT process contains spiral cutting and fly cutting. The main difference of these two processes lies in the morphology of intermediate-frequency turning marks on the surface, which affects the convergence rules. The turning marks after spiral cutting are a series of concentric circles, while the turning marks after fly cutting are a series of parallel big arcs. Polishing results indicate that MRF polishing can only improve the low-frequency errors (L>10 mm) of a spiral-cutting KDP crystal. MRF polishing can improve the full-range surface errors (L>0.01 mm) of a fly-cutting KDP crystal if the polishing process is not done more than two times for single surface. We can conclude a fly-cutting KDP crystal will meet better optical performance after MRF figuring than a spiral-cutting KDP crystal with similar initial surface performance.