http://localhost:8081/jspui/handle/123456789/112 2026-10-20T19:50:20Z http://localhost:8081/jspui/handle/123456789/21713 Title: EVALUATION OF ADVANCED ON-SITE SEWAGE TREATMENT SYSTEM (JOHKASOU) UNDER STRESS CONDITIONS Authors: Garg, Aashutosh Abstract: The Johkasou system is a decentralized wastewater treatment technology implemented at the Indian Institute of Technology Roorkee (IIT-R) to treat domestic wastewater. This study assessed the Johkasou system's performance at the IIT-R campus. The system can treat 7 m3 of sewage per day. The outlet from Johkasou is routed to a tertiary unit consisting of a Pressure Sand Filter and an Activated Carbon Filter for additional treatment. The effectiveness of removing organic and nutrient waste, the system's ability to function under various stress circumstances, and the performance of Johkasou at low sewage temperatures were all factors assessed during the study. Results showed that the Johkasou system effectively treated the wastewater generated at IITR. The system's average effluent concentration for biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and total nitrogen (TN) was 9 mg/L, 24 mg/L, 8 mg/L, and 8.7 mg/L respectively. In order to find the ideal poly aluminum chloride (PAC) dosage for the successful removal of total phosphorous, we performed a jar test study with different dosing. We examined how well PAC dosing enhanced total phosphorous (TP) removal in Johkasou. The findings demonstrated that the total phosphorous in the effluent was removed below 1 mg/L at an ideal PAC dosage of 200 ppm. We concluded from the vacation stress analysis that the system approximately requires a recovery time period of about 48-72 hours to stabilize when there is no influent for eight consecutive days. From the functional stress analysis, we concluded that we could turn the blower off for around 4-6 hours a day without impacting the system's performance. This will contribute to a 25% reduction in electricity costs. Another study was conducted in which the system was fed with sewage for approximately three months, with temperatures varying from 16-20 °C with three different HRTs of 22, 26, and 31 hours. The results showed that increasing the HRT from 22 to 31 hours when the sewage temperature varies from 16-20 °C significantly improved COD, BOD, TSS, and TN removal efficiency. The removal efficiency of COD and BOD increased from 88% to 94% and 87% to 97%, while TSS and TN removal efficiency increased from 88% to 96% and 60% to 79%, respectively. In order to obtain the kinetic coefficients, three kinetic models, the first-order, second-order, and Stover-Kincannon Model, were applied to the system. The optimal HRT for the Johkasou system, when the sewage temperature varies in the range of 16-20 °C, was 31 hours. In comparison, for temperatures in the range of 24-27 °C, Johkasou can be operated at 22 hours HRT, providing the highest removal efficiency for all the parameters tested. Johkasou's experimental data were used in substrate removal models to determine the rate of change in COD and TN concentrations. This study observed that the second-order substrate removal model fits well for COD with R2 = 0.9882, and Stover-Kincannon Model fits well for TN with R2 = 0.9703. Based on the kinetics analysis, the performance of the reactor can be optimized. It also offers the system's reaction rate and serves as a foundation for reactor design and operation. The wastewater treatment cost is Rs 23/m3, including electricity, cleaning and maintenance, chemical, and desludging costs. Overall, the research points to the Johkasou system as a viable option for decentralized wastewater treatment, particularly in locations with restricted access to centralized treatment facilities. 2023-05-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21712 Title: WASTE METAL CUTTING FLUID/COOLANT TREATMENT BY ELECTROCOAGULATION Authors: Dayal, Deen Abstract: The purpose of this study is treating the waste metal cutting fluid, popularly known as Coolant with the help of electrocoagulation treatment process. Mineral oil, surfactant, anti-rust, anti foam, and biocide make up around 20-70% of coolant emulsion. Cutting fluid is created by combining this emulsified oil with water in a ratio of 1:20 to 1:30. Due to machining operations the coolant contaminated by dust particles, small metal pieces, and bacteria. Before disposal, waste coolant must be treated because the emulsion does not break down naturally in water bodies and forms a film on the surface that prevents natural oxygen mixing, that blocks the light path, and contains toxic materials like heavy metals, biocides, oil, and more. These could endanger aquatic life. Therefore, EC method is being tested for removal purpose of mineral oil emulsion and other pollutants from the waste metal cutting fluid, by precipitating and floating method. Aluminium (Al) electrode was used as sacrifice electrodes and as passive electrodes. Four used sample of metal cutting fluid had obtained from SIDCUL and BHEL, Haridwar. In waste/used coolant, the initial oil concentration was about 2.66 - 40g/L (0.26 - 4%) and the COD ranged from 70000mg/L to 120000mg/L. Using electrocoagulation, in the experiment's COD was reduced in some cases up to 778mg/L. In a batch mode experiment, coolant sample was obtained in a vessel, 1.5g/L NaCl was added to increase coolant conductivity, and observations were taken at various time intervals. Energy requirement found between 0.187 to 0.62 kWh/kg COD removal and electrode consumption was found 0.007 – 0.35kg/m3. Average cost including energy and electrode cost per kl treatment of coolant about 250 and is go upto 800 Rs/kl. The results shows that the electrolysis time, current density, and surface charge (Zeta potential) are the three most essential variables in eliminating COD from cutting oil by EC, by using an aluminium electrode. According to the results, COD removal levels are insufficient to meet discharge regulations in inland water (permissible limit max COD 50mg/L, in India). So, EC would be ideal as a primary treatment that another treatment, like a biological, filtration could follow. 2023-05-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21711 Title: MACHINE LEARNING-BASED APPROACHES FOR MODELING VARIABILITY IN INFLUENT AND EFFLUENT QUALITY AT WASTEWATER TREATMENT PLANTS Authors: Ranjan, Gaurav Abstract: A rigorous analysis was performed using machine learning techniques such as regression trees, generalized linear models (GLM), and random forests to identify the relationships between climatic variables and quality parameters of two wastewater treatment plants located in very different geographical locations namely Agra, Uttar Pradesh, India and Colorado, US. Regression trees were used to classify the quality parameters based on climatic variables (temperature and precipitation) and time lagged values of the considered quality parameter. Temperature was identified as a major factor in classifying the influent values of quality parameters by showing a nearly inverse relationship, indicating that seasonal variation is seeming to take place. A correlation was developed between influent BOD and influent NH3 values using regression trees and time series analysis. The effect of extreme weather events was observed for the Agra plant (located in a tropical region), showing that extreme heatwaves events may lead to non-compliance of effluent BOD values. However, some other extreme events like large amounts of rainfall will favor in reducing the effluent BOD values. Similar effects were not observed for the Colorado plant (located in a temperate region). Rather it was observed that comparing the performance of machine learning models, random forests showed a high level of accuracy for predicting effluent BOD with a range of 80-90 percent. Random forests also helped in predicting effluent ammonia with an accuracy of nearly 70 percent, without taking influent ammonia into consideration. The GLM model was also useful in predicting effluent BOD values but was not as efficient as random forests 2023-05-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21710 Title: IMPACT OF CLIMATIC VARIABLES ON INFLOW VOLUMES TO WASTEWATER TREATMENT PLANTS USING MACHINE LEARNING TECHNIQUES Authors: Pande, Hitesh Abstract: Although the precipitation has decreased over the last decade, intense rainfall events have become very frequent in India. It is generally assumed that the rainfall events do not affect the sewage inflow volume to the wastewater treatment plants having separate sewer system (Sanitary sewers). This study aims at determining the effect of Climatic Variables mainly Rainfall and Temperature to the wastewater inflow to the wastewater treatment plants with Separate Sewer system using Machine Learning methods. In our attempt to determine the relation between the Climatic Variables and Sewage Inflow volumes to the wastewater treatment plants, K-means clustering, Regression trees, Logistic Regression, Random Forests, Local regression and Artificial neural network were used. Models were applied on the daily wastewater inflow values received by IIT Roorkee 3 MLD Sewage treatment plant, Saliyar, nearby Roorkee, Uttarakhand 33 MLD Sewage treatment Plant and Colorado, US 25 MGD (94.635 MLD) STP, all having Sanitary sewer system and daily Rainfall and Temperature values for Roorkee were collected from National institute of Hydrology, Roorkee and Daily Precipitation and Temperature values for Colorado, US were taken from National Oceanic and Atmospheric Administration (NOAA) website. Regression trees were used to see the effect of climatic variables i.e. Rainfall and Temperature to the inflow to the treatment plants. Median correct characterization by regression trees for IIT Roorkee STP was around 66%, for Saliyar it was around 72% and for Colorado, US STP it was around 86%. Logistic regression and Random forest classification were used to predict the risk of surge of inflow to the plants and mean accuracy for both the models for both IIT Roorkee and Saliyar STP was around 85% and for Colorado, US STP it was around 97%. Local regression, Random forest regression and Artificial neural networks were used to predict the sewage inflow to the plants. Local Regression and Random forest were found to perform better than Artificial neural networks and showed median R-squared value of around 74% for IIT Roorkee STP, 80% for Saliyar STP and around 97% for Colorado, US STP. Considering that all collection systems feeding into a Wastewater treatment plant in India exclusively consist of sanitary sewers, this finding points to substantial storm water ingress into the sanitary sewers as well. The usage of machine learning models to demonstrate the effect of rainfall, predicting the risk associated and predicting the sewage influent values for certain values of Rainfall and Temperature, can be used for better designing of the wastewater treatment plant. 2023-05-01T00:00:00Z