(180a) A Modular Design Framework That Reduces the Capital Intensity of Small-Scale Chemical Processes
- Conference: AIChE Spring Meeting and Global Congress on Process Safety
- Year: 2019
- Proceeding: 2019 Spring Meeting and 15th Global Congress on Process Safety
- Group: Process Intensification
- Time: Wednesday, April 3, 2019 - 1:30pm-2:00pm
To reduce the capital intensity of small-scale chemical processes, we depart from the classic way of designing single plants in isolation and propose a new methodology that is based on simultaneous design and manufacturing of multiple MCPS with many common functionalities. Instead of customizing equipment for each specific plant, we specify a common design for the equipment that perform the same function across all MCPS. From a manufacturing perspective, designing multiple MCPS simultaneously is advantageous, since it reduces the fixed costs of all common and standardized small-scale modules through economies-of-numbers and lowers the overall capital intensity of each participating MCPS. We present a systematic design framework for modular manufacturing of chemical processes, which is based on a mixed integer nonlinear optimization (MINLP) formulation. The framework explores the common phenomena that occur in different processes and identifies equipment which can be commonly-designed and can therefore be produced in large numbers. The benefits obtained through common functionality-based equipment design are demonstrated through methanol and ammonia production processes. The results indicate that cost reduction in the range 2-9% can be obtained when the inherent commonalities in equipment design for methanol and ammonia processes are leveraged via economies of numbers.
To address variabilities in chemical industries, we apply the concepts rooted in agile design where a multi-product processing train is developed which is capable of operating at different modes with different active process configurations (APCs). This allows us to encounter various seasonal variabilities in terms of feedstock availability and quality. Furthermore, to address uncertainty, we develop a stochastic programming-based two-stage recourse model that obtains optimal plant design plant under uncertain product demands and prices to maximize expected overall profit.