| Activated sludge process is currently one of the main biological methods for wastewater treatment. It is a key point to correctly reflect various reaction processes and mechanisms in modeling establishment and process optimization in simulation procedure. So far, more and more researches are focused on the soluble microbial products (SMPs). SMP which is generated in wastewater treatment process is relative to biomass metabolism. It influences wastewater treatment process in various aspects, for instance, consisting of important part in effluent COD, affecting effluent discharge standard, limiting the lowest treatment threshold, etc. On the other hand, unlike the mechanism of initial storage then growth for substrate described in activated sludge model no.3 (ASM3) proposed by IWA, many researchers found that simultaneous substrate storage and growth happened. Hence, the aim of this project is to propose a new activated sludge model with the above two mechanisms involved and further to calibrate its availability.In this project, firstly, a new proposed SMP-ASM3 model was established with the combination of SMP concept into ASM3. In which SMP was categorized into two parts:one was substrate utilization associated products (UAP) which is related to both substrate utility and biomass growth, and its producing rate is in proportion to substrate utilization rate. The other is biomass associated products (BAP) which is related to biomass respiration, and its producing rate is in proportion to biomass concentration. And the expression of SMP degradation is based on Monod model. Secondly, SMP-ASM3 model was further modified into SSSG-SMP-ASM3 model by combination of the simultaneous substrate storage and growth mechanism into SMP-ASM3. In which storage and growth occur simultaneously in the feast phase, and only after the depletion of the primary substrate the microorganisms would utilize the stored polymers as a carbon and energy source in the famine phase. Then, based on SSSG-SMP-ASM3 model, a further ASMP model was focused on the evaluation of the simultaneous storage and growth process, which indicated that the consumption of substrate by biomass should contain three processes which occurred simultaneously, i.e. substrate storage, biomass ditrect growth on substrate and biomass growth on storage products. In addition, from the practical application point of view, the mechanism of SMP degradation was simplified, i.e. SMP is first hydrolyzed into small molecular weight organic matters before being utilized by biomass. The reason for the assumption was due to the big molecular weight of SMP that can not get through the cell membrane.It is necessary to assess and indentify the high sensitive model parameters in order to provide guidances for model calibration. In this study, regional sensitivity analysis (RSA) was used to evaluate the effects of parameter random disturbances on effluent quality. It was found that the parameters fSTO,Y1,O,kH,YSTO,O,kSTO,O,kUAP,NO,KA,NO,K1,kHUAP,μH2,O,kUSTO,NO, Y1,NO,kSTO,NO,KA,O,kBAP,O,μH,O,μH,NO,KA,ALK,bH,NO,kUAP,O,μH2,NO and K2 affected evidently on effluent COD; the parametersμA,μH1,μH2,fSTO, kSTO,kSTOU and kSTOB contributed more to oxygen profile;the parameters fSTO,μA, YSTO,O, Y2,O, Y1,O,kUAP,NO,kUSTO,NO, kH,μH2,O,μH,NO,KO, Y1,NO,KNO,kSTO,NO,bH,O,YA, KA,ALK,kBAP,NO,KA,NH and kSTO,O affected evidently on effluent SNH and the parameters fSTO, kH,μA,KA,ALK, KO,kHUAP,μH2,O,kSTO,NO, Y1,O, Y1,NO,KA,NH, K1,μH2,NO,KA,NO and K2 took more responsibility for SNO concentration.It is an important part in model evaluation and calibration procedure to assure the model's validation and accuracy in its application. In this study, a very applicable method was proposed for the new proposed model calibration which mainly consists of the following eight steps:a. identify the sensitive parameters; b. classify the influent components using experiments according to the model requirements; c. evaluate the model mechanism with OUR tests; d. calibrate and assess the proposed model with activated sludge processes; e. change the operation parameters and compare the experimental data with the simulated results by the calibrated model; f, if there is a obvious deviation, use the data in step e to recalibrate the model, and use the data in step d to check the model simulation; g. validate the calibrated model with another kind of activated sludge process with the same influent characteristic; h. if there is a obvious deviation in step g, repeat steps d-f, till the validation is successful.According to the new proposed protocol, the evaluations of models with data from OUR tests have been carried out. The results indicated that ASMP model was more accurate than SSSG-SMP-ASM3 in simulating OUR dynamic variation (the correlation coefficient R were 0.893 and 0.848, and the sum of squared errors (SSE) were 0.013 and 0.023) respectively; ASMP model was more accurate than ASM3 in predicting SCOD dynamic variation (R were 0.981 and 0.977, and SSE were 79.2 and 354.9 respectively), which means:a) there are three processes occurred simultaneously for biomass growth, i.e. substrate storage, growth direct on substrate and growth on storage products; b) it is necessary to add SMP mechanism into ASM3. Meanwhile, models (ASMP, SMP-ASM3 and ASM3) have also been calibrated and evaluated with data from a lab-scale sequential batch reactor (SBR). The simulations of SCOD, SNH and SNO dynamic variations during one cycle of SBR and effluent SCOD, UAP, BAP, SNH, SNO, TN and mixed liquid suspended solid (MLSS) were focused on. The results indicated that ASMP was more accurate than SMP-ASM3 and ASM3 in predicting the dynamic variations of SCOD (R were 0.939,0.876 and 0.929; SSE were 737.1,1757.7 and 8370.3 respectively), SNH (R were 0.992,0.991 and 0.979; SSE were 1.72,12.85 and 12.12 respectively) and SNO (R were 0.992,0.972 and 0.952; SSE were 1.19,10.8 and 11.53 respectively), and in predicting of effluent SCOD (SSE were 0.25,4.0 and 571.2), UAP (SSE were 0.01,0.49 and—), BAP (SSE were 0.16,1.69 and—), SNH (SSE were 0.01,0.09 and 0.09), SNO (SSE were 0.09,4.41 and 4), TN (SSE were 1.69,8.41 and 7.84) and MLSS (SSE were 7395,67081 and 123000). In addition, the effluent data (SCOD, UAP, BAP, SNH and SNO) from a lab-scale completely stirred tank reactor (CSTR) with same influent characteristic were used to validate the calibrated ASMP model by SBR data, and the comparative results between measured and simulated values demonstrated the well calibration of the model as the SSE were 0.04,0.25,0.09,0.01 and 1.69 respectively. Based upon above results, the optimization of SBR process was conducted with ASMP model and the predicted effluent qualities under various operational conditions were investigated.Eventually, ASMP and ASM3 models were applied to simulate the dynamic variations of effluent from March to June of one Wastewater Treatment Plant (WWTP) in Shanghai. The simulated results suggested that ASMP was more accurate than ASM3 in predicting effluent COD,SNH and SNO, as the average deviations between measured and simulated values by ASMP were 0.8 (-16.1-12.5),0.3 (-3.6-7.6) and-5.4 (-21.7-1.7) less than that by ASM3 which were 8.6 (-6.9-19.8),2.0 (-4.3-8.5) and-7.0 (-28-1.7), respectively. It also indicated that SMP consisted to part of effluent COD (about 25%) and be an important component in effluent COD.
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