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A simple mathematical model is developed for the prediction of Metribuzin removal efficiency through adsorption using activated carbon of olive-waste cake for any combination of input conditions. Based on earlier experimental results, factors from three independent variables (pH, initial Metribuzin concentration and adsorbent dose concentration) were incorporated in the model. All the factors are multiplied to derive a combined diminishing factor, which is multiplied with maximum achievable removal efficiency. It is found that although the model results are having good correlation (0.92) with the experimental results, those are slightly away from the ideal line. Through the introduction of an adjustment factor, model predictions are closely matching with the measured values having a correlation coefficient of 0.96. The primary model predicted results are having standard errors as RMSE = 6.34, MAE = 5.99 and RAE = 0.07, whereas the same error statistics of the adjusted model are 1.97, 1.71 and 0.01, respectively. Such modelling technique will predict removal efficiency for any combination of input parameters, which at times are required to be changed for other constraints. [ABSTRACT FROM AUTHOR]

Excessive release of methylene blue (MB) in the environment and its potential infusion to human body may cause several health issues such as blurred vision, confusion, hallucinations and seizures. Based on experimental results on MB removal efficiency through adsorption using activated carbon of Parthenium hysterophorus, a simple mathematical model is developed for the prediction of MB removal efficiency for any combination of input conditions. Based on earlier experimental results, four independent variables such as pH, contact time, initial MB concentration and dose concentration were considered for the model. Based on individual experimental results, a factor is proposed for each independent variable. Eventually, all the factors are multiplied with maximum achievable removal efficiency to predict a removal efficiency under specific conditions. It is found that the developed model can accurately predict the higher range of efficiency while underestimates during lower range of efficiency. To overcome this drawback, the developed model was further fine-tuned with an adjustment factor, through which model predictions were turned out to be much better. Standard errors of the original model's estimations are RMSE = 10.09, MAE = 7.08 and RAE = 0.12. After the mentioned adjustment, the same errors were lowered by 60%, 57% and 83%, respectively. Such modelling approach is very useful for the decision-makers on deciding input variables for industry-scale implementations. Similar mathematical modelling can be adopted for other pollutants and for other adsorbents. [ABSTRACT FROM AUTHOR]