Smart control of heavy metal adsorption onto LDC wastes for Industry 4.0 applications

• Smart adsorption optimizes water treatment for SDG 9 & Industry 4.0 targets. • RSM & MLAs enhance predictive accuracy in heavy metal removal efficiency. • TRF, IBK, & FMLP models achieve >0.92 CC & >0.8 recall/precision indicators. • Knowledge Flow model enables real-time process control & alarm systems. • pH 5 & adsorbent mass 8.67-10 g/L maximize heavy metal removal efficiency. Due to the complexity and the need for automation in adsorption systems, this study develops a decision-making model using 20 Machine Learning Algorithms (MLAs), Response Surface Methodology (RSM), and lab tests. Inputs include pH, metal type, concentration, adsorbent mass, and time; output is removal percentage for process control. The best-performing models (Tree Random Forest: TRF, lazy Instance-Based K: IBK, and Function Multilayer Perceptron: FMLP) achieve high accuracy for prediction of Removal Percentage (RP) of heavy metals with more than 0.92 correlation coefficient and more than 0.8 recall/precision indicators. As a novelty of this study, a decision-making model for the heavy metal adsorption process onto waste materials is developed for the first time using the Knowledge Flow (KF) platform in WEKA software, incorporating real-time data to improve process control and operational efficiency. The results demonstrated that the cation type and pH, with a P-value < 0.001, are the most significant factors affecting the RP. To achieve maximum RP, the pH should be set to 5, and the adsorbent amount should be in a range of 8.67-10 g L −1 , regardless of the initial concentration and type of ions (Pb²⁺, Mn²⁺, and Co²⁺). Then, evaluating MLAs showed that TRF, with a correlation coefficient exceeding 0.96, performs best for predicting RP, potentially reaching 0.99 at a split percentage around 80%. TRF uses real-time sensor data in water treatment systems to anticipate RP up to 98.75% and to set off alarms for RP below 80% using the KF principle.