(33e) Towards a System for Optimal Multi-Floor Process Plant Layout Design
AIChE Annual Meeting
2020
2020 Virtual AIChE Annual Meeting
Computing and Systems Technology Division
Software Tools and Implementations for Process Systems Engineering
Monday, November 16, 2020 - 9:00am to 9:15am
Over the past three decades, there has been substantial development in mathematical optimisation models to address a range of features in process plant layout design. Researchers have made considerations for single and multiple floor equipment placements, grouping of equipment items in production sections, descriptions for tall equipment, and the inclusion of costs to capture safety, financial risks, and protection device installations. In line with these, techniques have been applied using exact, heuristic, metaheuristic or hybrid approaches to obtain optimal solutions in modest computational times.
This work proposes a computer-based platform which provides a user-friendly implementation of some of these models (Ejeh et al., 2019) to obtain optimal multi-floor layout configurations. Layout features considered include connectivity costs by pipes, pumping costs, area-dependent land area and construction costs. Using user-defined inputs of equipment geometry and connectivity structure and cost data, the python-based, graphical user interface (GUI) platform obtains single or multi-floor layout designs that minimise the total layout costs. Users of the platform can obtain informative capital and operational costs of proposed layout designs with little or no expert knowledge. It also provides layout engineers with fast, minimal-cost base designs which can be further customised for a particular process plant.
References
Ejeh, J. O., Liu, S., Chalchooghi, M. M., & Papageorgiou, L. G. (2018). Optimization-based approach for process plant layout. Industrial & Engineering Chemistry Research, 57(31), 10482â10490. https://doi.org/10.1021/acs.iecr.8b00260
Ejeh, J. O., Liu, S., & Papageorgiou, L. G. (2019). Optimal layout of multi-floor process plants using MILP. Computers & Chemical Engineering, 131, 106573. https://doi.org/10.1016/j.compchemeng.2019.106573