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.

环境中亚甲蓝 (MB) 的过量释放及其对人体的潜在渗透可能会导致若干健康问题，例如视力模糊、意识模糊、幻觉和癫痫发作。基于Parthenium hysterophorus活性炭吸附去除 MB 效率的实验结果，开发了一个简单的数学模型，用于预测任何输入条件组合的 MB 去除效率。基于早期的实验结果，模型考虑了四个独立变量，例如 pH 值、接触时间、初始 MB 浓度和剂量浓度。根据个别实验结果，为每个自变量提出一个因子。最终，所有因素乘以最大可达到的去除效率以预测特定条件下的去除效率。发现所开发的模型可以准确地预测较高的效率范围，而在较低的效率范围内低估。为了克服这个缺点，开发的模型进一步微调了一个调整因子，通过它模型预测结果要好得多。原始模型估计的标准误差为 RMSE = 10.09、MAE = 7.08 和 RAE = 0.12。经过上述调整，同样的误差分别降低了 60%、57% 和 83%。这种建模方法对于决策者决定工业规模实施的输入变量非常有用。对于其他污染物和其他吸附剂，可以采用类似的数学模型。