The optimum conditions and effectiveness of extraction of extracellular polymeric substances (EPS) from activated sludge in a membrane bioreactor(MBR) were studied under such conditions, thermal, acid, alkaline treatment methods, and it was concluded that both thermal treatment (80℃,45 min) and EDTA bi-sodium treatment were effective.The sludge cell was damaged seriously by NaOH treatment (1 mol·L-1,2 h), but when formaldehyde was added, the nucleic acid concentration was reduced by 21.5%, and the damage to sludge cell was then decreased.
This paper describes the procedure of using the GM (1,1) weighted Markov chain (GMWMC) to forecast the utility water supply, a quantity that usually has significant temporal variability. The GMWMC is formulated into five steps: (1) use GM (1,1) to fit the trend of the data, and obtain the relative error of the fitted values; (2) divide the relative error into ‘state’ data based on pre-set intervals; (3) calibrate the weighted Markov chain model: herein the parameters are the pre-set interval and the step of transition matrix (TM); (4) by using auto-correlation coefficient as the weight, the Markov chain provides the prediction interval. Then the mid-value of the interval is selected as the relative error for the data. Upon combining the data and its relative error, the predicted magnitude in a specific time period is obtained; and, (5) validate the model. Commonly, static intervals are used in both model calibration and validation stages, usually causing large errors. Thus, a dynamic adjustment interval (DAI) is proposed for a better performance. The proposed procedure is described and demonstrated through a case study, which shows that the DAI can usually achieve a better performance than the static interval, and the best TM may exist for certain data.
