Complete solids retention activated sludge process.

Waste water treatment system plays a vital role in controlling pollution of natural water bodies like lake, pond, river etc., by Municipal and Industrial effluents. Different industrial effluents play its own role in contaminating the water bodies which in turn creates huge impact for aquatic and terrestrial life. From past studies we understood that, all the sewage treatment systems have a secondary treatment step which are mainly driven by Bacterial oxidation or in other terms can be pronounced as Biological augmentation, Biological calcification etc., In environmental engineering term this bacterial growth will be pronounced as MLSS (Mixed Liquor Suspended Solids) and MLVSS (Mixed Liquor Volatile Suspended Solids). MLVSS will be an inclusive part of the MLSS and also can be sorted as live bacterial cells which can really does the oxidation process in the secondary treatment step of Sewage treatment plant. Hence, this study was performed to evaluate the percentage or concentration of MLVSS available in the total value of MLSS. For this study aeration tank water samples were collected from 6 different STP capacities from 6 different areas. All the samples were tested for MLSS and MLVSS concentration with the available standard method of drying. Drying with 105° C in oven gives the value of MLSS and drying with 550° C in furnace gives the value of MLVSS. With all the tested samples the concentration of MLVSS from the total MLSS was evaluated and standardized.

The performance of an activated sludge wastewater treatment plant depends on bioflocculation that is monitored by physical measurements such as the sludge volume index (SVI) and mixed liquor suspended solids (MLSS). The estimation of SVI and MLSS has been proposed using image analysis based modeling which is time-efficient and valid for multiple plants operating in different states. The methodology includes the sequence of image acquisition using bright-field microscopy, a robust segmentation of flocs, partitioning of flocs based on different ranges of their equivalent diameters, extraction of morphological features, and modeling of SVI and MLSS using the features. It is proposed that bright-field microscopy at lower magnification to capture the flocs is sufficient to model SVI and MLSS. A robust approach for image segmentation is adopted by integrating state-of-the-art image segmentation algorithms. It is hypothesized that flocs in different ranges of equivalent diameter respond differently to the variation in the operating state. Hence, flocs and their respective image analysis features are categorized based on the range of equivalent diameter. Finally, stepwise regression is used for feature selection and model identification to explore the feasibility of generalization of models to multiple plants in different states regarding SVI and MLSS.

Sequencing batch reactor (SBR) was assessed for direct co-treatment of old landfill leachate and municipal wastewater for chemical oxygen demand (COD), nutrients and turbidity removal. Nitrogen removal was achieved by sequential nitrification and denitrification under post-anoxic conditions. Initially, SBR operating conditions were optimized by varying hydraulic retention time (HRT) at 20% (v/v) landfill leachate concentration, and results showed that 6 d HRT was suitable for co-treatment. SBR performance was assessed in terms of COD, ammonia, nitrate, phosphate, and turbidity removal efficiency. pH, mixed liquor suspended solids, mixed liquor volatile suspended solids (MLVSS), and sludge volume index were monitored to evaluate stability of SBR. MLVSS indicated that biomass was able to grow even at higher concentrations of old landfill leachate. Ammonia and nitrate removal efficiency was more than 93% and 83%, respectively, whereas COD reduction was in the range of 60–70%. Phosphate and turbidity removal efficiency was 80% and 83%, respectively. Microbial growth kinetic parameters indicated that there was no inhibition of biomass growth up to 20% landfill leachate. The results highlighted that SBR can be used as an initial step for direct co-treatment of landfill leachate and municipal wastewater.

Effect of pre-chlorination on bioelectricity production and stabilization of excess sludge by microbial fuel cell.

Pilot-scale study and biochemical verification of salt-tolerant catalyst Fe-Bi@γ-Al2O3 for catalytic ozonation of high-salinity wastewater

Operation performance of an A/A/O process coupled with excess sludge ozonation and phosphorus recovery: A pilot-scale study

This study proposes a new approach for integrated technology of ultrasonic and membrane for a palm oil mill effluent treatment. This study evaluated the performance of the new design of ultrasonic membrane anaerobic system (UMAS) when a palm oil mill effluent (POME) introduces this approach. To fit kinetic study, six steady states were investigated and the results have shown that the mixed liquor volatile suspended solids (MLVSSs) range from 10,400 to 17,350 mg/l while the mixed liquor suspended solids (MLSSs) range from 13,800 to 22,600 mg/l. Three kinetic models of Monod, Contois, and Chen and Hashimoto were used to evaluate the integrated system at organic loading rates ranging from 1 to 15 kg COD/m3/day. The percentage efficiency of COD removal was from 92.8 to 98.3%, and hydraulic retention time (HRT) was from 500.8 to 8.6 days. The influent COD concentrations of the POME ranged from 70,400 to 90,200 mg/l.The integrated technology of UMAS is a more attractive one as it avoids membrane fouling problems.

Feasibility of applying forward osmosis to the simultaneous thickening, digestion, and direct dewatering of waste activated sludge

Sludge management is a very relevant aspect in the operation of Wastewater Treatment Plants (WWTPs). In activated sludge systems, it is common to have daily (or continuous) monitoring of total suspended solids in the aeration tank (MLSS). If such control is not properly performed, it can cause solids to wash out in the secondary sedimentation tank or significantly impact BOD (Biochemical Oxygen Demand) and nitrogen removal. There are many commercially available systems which can provide real-time monitoring of solids (mainly optical or ultrasound sensors). Even though commercially available (usually with a high cost), there are still issues related to the use of such sensors. The most important one is the progressive accumulation of solids, which cause measurement errors. In this work, the authors investigated the application of a low-cost US sensor for MLSS (mixed-liquor suspended solids) monitoring in two full-scale activated sludge WWTPs. The tested sensor was similar to a previously described device, which had been previously employed in a pilot-scale UASB reactor in Brazil. The main differences were related to an integrated treatment and acquisition system which allowed real-time treatment of the US wave as well as data acquisition at a predefined time. The values generated by the sensor were compared with a commercial optical sensor installed in the same WWTP and double-checked with periodic gravimetric tests. The results at a Leeuwarden WWTP showed that the measurements of the US sensor, the optical sensor, and gravimetric test did not present significant differences during the test period at a significance level of 5%. Absolute errors were on average 0.04% (US sensor) and 0.03% (optic sensor) of MLSS compared to the gravimetric test. Although the use of the tested US sensor for monitoring solids in WWTP is promising, there are still several improvements that need to be made to the sensor. These include implementing a more precise calibration frequency, establishing a cleaning routine, and preventing sensor fouling. Furthermore, the sensor still needs a more thorough cost–benefit analysis, which would help assess the practicality of implementing this technology in various WWTPs.

Application of a membrane bioreactor for treating explosives process wastewater

Real-time quantification of activated sludge concentration and viscosity through deep learning of microscopic images.

Sludge rheological and physiological characteristics in a pilot-scale submerged membrane bioreactor

Aims: The present study was carried out to determine biokinetic coefficients of a pilot-scale adsorption-bio-oxidation process. Materials and Methods: The pilot plant was simulated North wastewater treatment plant, Isfahan, Iran. The pilot plant was operated 135 days under different mixed liquor suspended solids (MLSS) concentrations in aeration tank. In each phase, MLSS value was kept constant to reach a steady state condition. B-stage has a higher hydraulic retention time and SRT than A-stage. Also, in order to determine influence of biokinetic parameters on the effluent substrate concentration, a sensitivity analysis was performed. Results: The coefficients Y, K d , K S , and μmax of A-stage were 1.34 mg VSS/mg sCOD, 0.17 d−1 , 8.61 mg/L, and 2.78 d−1 , respectively. Also, Y, K d , K S , and μmax of B-stage were 0.74 mg VSS/mg sCOD, 0.12 d−1 , 3.34 mg/L, and 71.94 d−1 , respectively. Sensitivity analysis showed that in the A-stage, all coefficients are directly proportional to the effluent sCOD concentration. In the B-stage, K d and K S are directly proportional, but μmax was inversely proportional to the effluent sCOD concentration. Conclusions: All coefficients were in the range of activated sludge coefficients that are mentioned in the literature, except μmax and K S of B-stage. However, K S value of B-stage was close to the desired range. Sensitivity analysis showed that μmax and K S have the most influence on effluent substrate concentration (sCOD).

Performance of submerged anaerobic membrane bioreactor at different SRTs for domestic wastewater treatment

Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.