Machine Learning-Based Prediction of Adsorption Capacity of Metal-Doped and Undoped Activated Carbon: Assessing the Role of Metal Doping.

This research developed five ensemble-based machine learning (ML) models to predict the adsorption capacity of both pristine and metal-doped activated carbon (AC) and identified key influencing features. Results indicated that Extreme Gradient Boosting (XGB) model provided the most accurate predictions for both types of AC, with metal-doped AC exhibiting 1.7 times higher adsorption capacity than pristine AC showing 254.66 and 148.28 mg/g, respectively. Feature analysis using SHAP values revealed that adsorbent characteristics accounted for 53.5 % of the adsorption capacity in pristine AC, while experimental conditions were crucial for metal-doped AC (61.3%), with surface area and initial concentration being the most significant features, showing mean SHAP values of 0.317 and 0.117, respectively. Statistical comparisons of adsorbent characteristics between pristine and metal-doped AC showed that metal doping significantly altered surface area (p-value = 0.0014), pore volume (p-value = 0.0029), and elemental composition (C% (p-value = 3.9513*10ˆ-7) and O% (p-value = 0.0007)) of AC. Despite the reduction in surface area and consistent pore volume after metal doping, the enhanced adsorption capacity of metal-doped AC was attributed to increased oxygen content from 10.89% to 17.28 % as mean values. This suggests that oxygen-containing functional groups play a critical role in the improved adsorption capacity of metal-doped AC. This research lays the groundwork for optimizing AC adsorbents by identifying key factors in metal-doped AC and suggest further studies on the interaction between specific metal dopants and resulting functional groups to improve adsorption capacity and reduce repeated labor work.

Intelligent inventory management with autonomation and service strategy.

The manufacturer's service to the customer is one of the critical factors in maximizing profit. This study proposes the innovative (Q, r) inventory policy integrated with autonomated inspection and service strategy for service-dependent demand. First, an advanced autonomated inspection makes the product error-free. Therefore, this makes customers more satisfied and increases profit. The proposed model decides the optimal investment for such autonomated inspection. Second, three types of services are considered in the study: unpaid, partially paid, and fully paid services. Each type of service has a different service level and the amount of the customer's payment. Our model finds the optimal service strategy based on the variable conditions along with the optimal quantity and reorder level of inventory policy. Numerical analyses are made for different service strategies, along with a sensitivity analyses for various critical parameters. Results show that the full paid service is 84.88% beneficial compared to the unpaid service, and the autonomated inspection policy is 5.02% beneficial compared to the traditional ones. The increase in unit servicing costs always increases the profit of the company.