Fergyanto E Gunawan’s scientific contributions

Catalyst deactivation has become a great concern in an industry with heterogenous catalystbased production. An accurate model to predict catalyst performance is needed to optimize the maintenance schedule, avoid an unplanned shutdown, and ensure reliable operation. This research work applies a machine learning model to predict catalyst deactivation based on actual data from relevant multitube-reactor sensors. The product conversion is a crucial indicator of the catalyst performance degradation over time. Random forest regression (RFR) algorithm is chosen to construct the model. Hyperparameter tuning is applied and shows improvement over the default model. The result showed that the RFR model could predict the conversion as a time series function. The feature importance analysis shows the most influencing factor and facilitates the model interpretation.

One of the organization’s logistic business models is to deliver the goods from warehouses (depots) to many retail stores in order to fulfil the customer demand and achieve the KPIs of logistic department. The purpose of the paper is to provide an optimum route of delivery routing operations. The problem was a part of Balanced Allocation Multi Depot Vehicle Routing Problem (BAMDVRP) as a multi-objective mixed integer programming, which solution could be determined to provide an optimum logistic delivery routing and the efficient resources to boost the logistic performance and uplift the overall supply chain sustainability.