Combining Coagulation and Electrocoagulation with UVA-LED Photo-Fenton to Improve the Efficiency and Reduce the Cost of Mature Landfill Leachate Treatment

Modelling coagulant dosage in drinking water treatment plant using advance machine learning model: Hybrid extreme learning machine optimized by Bat algorithm

Despite the high importance of coagulation process in drinking water treatment plant (DWTP), challenge remains in effectively linking raw water quality measured at the inlet of the DWTP with coagulant dosage rate. This study proposes an integral modelling framework using hybrid extreme learning machine and Bat metaheuristic algorithm (ELM-Bat) for modelling coagulant dosage rate using water temperature, pH, specific conductance, dissolved oxygen, and water turbidity. The aluminum sulphate (Al₂ (SO₄)₃.18H₂O) coagulant is determined using conventional Jar-Test procedure. Results obtained using the hybrid ELM-Bat were compared to those obtained using standalone ELM, outlier robust extreme learning machine (ORELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), and kernel extreme learning machine (KELM). First, the models have been calibrated during the training stage and in a second stage; they are validated using various statistical metrics, i.e., RMSE, MAE, the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). We found that the hybrid ELM-Bat was significantly more accurate and it has yielded accuracy higher than all other models. During the validation stage, the R and NSE values calculated using the ELM-Bat were ≈0.959 and ≈0.918 exhibiting an improvement rates of approximately (≈15.26% and ≈33.82%), (≈10.35% and ≈21.92%), (≈14.98% and ≈31.89%), (≈7.63% and ≈16.35%), (≈10.99% and ≈23.05%), compared to the values obtained using the ELM, OPELM, OSELM, KELM and ORELM, respectively. Besides, the new ELM-Bat model has shown to have high predictive capabilities, which can be used optimally for calculating the optimal coagulant dosage with high accuracy.

Micro- and Macroelements Content of Plants Used for Landfill Leachate Treatment Based on Phragmites australis and Ceratophyllum demersum

One of the key problems associated with the functioning of landfills is the generation of leachate. In order to reduce their negative impact on the environment, various treatment technologies are applied. Among them, solutions based on the use of phytotechnology deserve special attention. The aim of this study was to evaluate the impact of landfill leachate on the content of micro- and macroelements in plant material. The research was carried out in four municipal waste landfills located in Poland. Emergent macrophytes (P. australis) and submergent macrophytes (C. demersum) were used in this research. The migration and distribution of pollutants reaching the roots and shoots of P. australis from water solutions were also studied. The concentrations of heavy metals in the studied plants were low in all analysed cases. Higher metal contents could often be observed in roots rather than in shoots, but these differences were insignificant. The chemical composition of the studied plant samples was primarily related to the source of origin of the treated leachate (landfill), as clearly demonstrated by cluster analysis. In the conducted studies, no important differences were noted in the accumulation of the studied components between submergent plants (C. demersum) and emergent macrophytes (P. australis).