(401b) A Simultaneous Optimization Approach for Synthesis and Design of Process and Water Networks
AIChE Annual Meeting
2014
2014 AIChE Annual Meeting
Computing and Systems Technology Division
Process Design II
Tuesday, November 18, 2014 - 3:34pm to 3:53pm
Recently, significant efforts are being made to reduce freshwater consumption and wastewater generation in process industries. These efforts are regarded as critical concerns among industrial practitioners due to the rise of freshwater and effluent treatment costs and stringent regulations. One of the more efficient ways to reduce freshwater consumption in the process is by reusing wastewater that is generated by the process or utility after being treated in the wastewater treatment plant to acceptable limits. This can be done by considering various treatment technologies and exploring numbers of alternatives. Furthermore, different process technologies and design alternatives are also being considered in the processing network to transform raw materials into products while reducing freshwater consumption and wastewater generation. In order to accomplish this goal, many design alternatives can be considered. One of the techniques used to represent the design space and identify best technology network is superstructure-based optimization techniques [1, 2].
Usually, in process synthesis and design, subsystems (e.g. water network and heat exchanger network) are dealt and solved sequentially or separately after process synthesis and design is performed and an optimal process flow sheet is identified. In this work, a simultaneous synthesis and design of process and wastewater networks or also known as a multi-network problem is presented. The integration of water consumption early at process synthesis and design stage is expected to reduce the consumption of freshwater and reusing more processed water. In this end, we address the challenge to manage the complexity of early-stage decision making in synthesizing and designing process and wastewater networks.
In order to address these challenges, a systematic framework recently developed by Quaglia et al. [1] is used as a basis and expanded use of the superstructure-based optimization approach for the optimal synthesis and design of processing plant network that is connected with a wastewater treatment network. A solution strategy to solve the multi-network problem accounts explicitly the interactions between the process and wastewater networks by selecting appropriate technologies and alternatives in order to convert raw materials into products and produce cleaned water to be reused in the process. The strategy accounts explicitly for the interactions between the process and water networks (so called as a network within a network) via selection of appropriate raw materials, technologies and alternatives for process and water treatment as well as products. The systematic approach is used to manage the difficulty and solving simultaneously process synthesis and water synthesis network problems with respect to economics, resources consumption and sustainability.
A new superstructure is formulated for the simultaneous synthesis of the process and water networks in order to achieve this task optimally and efficiently. All possible alternatives with respect to the topology of the process and wastewater network are represented in the superstructure at different process and treatment tasks. The resulting synthesis-design problem size is large and complex. The optimization problem is formulated as a Mixed Integer Non Linear Programming (MINLP) and solved in GAMS under different objective function scenarios (e.g. minimize total cost, minimize waste etc.) and constraints (e.g. effluent discharge limits, process pollutant limit etc.). The applicability of the systematic approach is demonstrated using a conceptual case study, especially developed to test all the features of the solution approach.
Reference:
[1] A. Quaglia, B. Sarup, G. Sin, R. Gani, 2012. Computers and Chemical Engineering, 38, 213.
[2] H. Yeomans, I.E. Grossmann, 1999. Computers and Chemical Engineering, 23, 709.