Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants.

In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (N2O) and carbon dioxide (CO2) as well as effluent quality (COD - chemical oxygen demand, NH4-N - ammonia, NO3-N - nitrate) in activated sludge method. For methane (CH4), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model. Additionally, scenarios were analyzed to assess and reduce the GHGs emissions related to the biological processes. A real WWTP, with a population equivalent (PE) of 125,000, was studied for the validation of the hybrid model. A global sensitivity analysis (GSA) of the MCM and a ML model were implemented to assess GHGs emission mechanisms the biological reactor. Finally, an early warning tool for the prediction of GHGs errors was implemented to assess the accuracy and the reliability of the proposed algorithm. The results could support the wastewater treatment plant operators to evaluate possible mitigation scenarios (MS) that can reduce direct GHG emissions from WWTPs by up to 21%, while maintaining the final quality standard of the treated effluent.

Application of a generalized hybrid machine learning model for the prediction of H2S and VOCs removal in a compact trickle bed bioreactor (CTBB).

This study presents a generalized hybrid model for predicting H2S and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification model, and (ii) prediction of K <100%, based on inlet concentration, time of day, pH and retention time. Global sensitivity analysis (GSA) was used to test the relationships between the inputs and outputs of the K-NN model. The results from classification model simulation showed high goodness of fit for the classification models to predict the removal of H2S and VOCs (SPEC = 0.94-0.99, SENS = 0.96-0.99). It was shown that the hybrid K-NN model applied for the "Klimzowiec" WWTP, including the pilot plant, can also be applied to the "Urbanowice" WWTP. The hybrid machine learning model enables the development of a universal system for monitoring the removal of H2S and VOCs from WWTP facilities.

Recovery of Cerium Salts from Sewage Sludge Resulting from the Coagulation of Brewery Wastewater with Recycled Cerium Coagulant.

Due to the high cost and limited sources of cerium coagulants, it is extremely important to take measures to recycle this raw material. This paper presents the new possibility of recovering cerium(III) chloride, cerium(III) sulphate, cerium(IV) sulphate, and potentially phosphate from sewage sludge (101.5 g/kg Ce and 22.2 g/kg total P) through a brewery wastewater treatment process using recycled CeCl3 as a coagulant. In order to recover the Ce and P, the sludge was subjected to extraction using an HCl solution. Optimal process conditions were determined by means of central composite design and response surface methodology (CCD/RSM) for three input parameters (HCl mass, reaction time, and extractant volume). Under optimal conditions (0.35 g HCl per 1 g of sludge, 40 min reaction time, extractant volume of 25 mL per 1 g of sludge), the highest efficiency obtained was 99.6% and 97.5% for Ce and P, respectively. Cerium(III) oxalate as Ce2(C2O4)3∙10H2O was precipitated from the obtained solution using H2C2O4 (99.97%) and decomposed into CeO2 (at 350 °C), which was afterwards subjected to a reaction with HCl (30%, m/m) and H2O2 (30%, m/m), which led to the crystallisation of CeCl3∙7H2O with a purity of 98.6% and a yield of 97.0%. The obtained CeO2 was also subjected to a reaction with H2SO4 (96%, m/m) and H2O2 (30%, m/m), which produced Ce2(SO4)3 with a yield of 97.4%. The CeO2 was also subjected to a reaction with only H2SO4 (96%, m/m), which produced Ce(SO4)2 with a yield of 98.3%. The filtrate obtained after filtering the Ce2(C2O4)3∙10H2O contained 570 mg/L of P, which enabled its use as a source of phosphorus compounds. The presented processes of Ce and potentially P recovery from sewage sludge originating from brewery wastewater contribute to the idea of a circular economy.