Two-stage Stochastic Optimization of Carbon Dioxide Supply Chain and Utilization Model through Carbon Dioxide Capturing Process

Abstract

Abstract At the present time, the emitted carbon dioxide (CO 2 ) becomes a serious problem of the world because CO 2 is a main greenhouse gas causing global warming. Therefore, several technologies have been developed in order to capture CO 2 , efficiently. An acid gas removal process, as known as AGRU unit, is a process that removes CO 2 and H 2 S from natural gas by absorber unit. To increase added value in captured CO 2 , many industries utilize CO 2 as a feedstock for plastic production and also methanol plant. Hence, CO 2 -utilization plant must be included in supply chain. In this research, mathematical programming is applied to manage captured CO 2 distribution through a supply-chain formulation. This supply chain consists of 4 nodes which are raw-natural-gas sources, AGRUs, manufacturers for CO 2 utilization; and end-use customers. Due to uncertain compositions of natural gas feed stream and customer demands, the operating cost and utility are dramatically affected. To handle this problem, stochastic programming helps design an uncertain-factor model and to find the optimal CO 2 supply chain solution. Moreover, investment cost of CO 2 -utilization plant is also concerned as a first stage of optimization. The model designs optimal material flow between nodes under the objectives of optimal investment cost and profit repayment in order to satisfy the customer demand. Two-stage stochastic optimization for CO 2 -supply-chain model with CO 2 capturing process using mixed-integer nonlinear programming (MINLP) is proposed. The proposed model minimizes overall costs under uncertainties in compositions of raw natural gas and customer demands to design the optimal supply chain. Stochastic programming is compared with deterministic programming and its result shows a supply chain in terms of optimal investment (from first stage) and operating decisions(second stage) in transportation cost, operating cost, purchase cost, and penalty cost.