Jeroen J. Jansen

7402865428

Publications - 2

Retrofit heat exchanger network optimization via graph-theoretical approach: Pinch-bounded N-best solutions allows positional swapping

Publication Name: Energy

Publication Date: 2023-11-15

Volume: 283

Issue: Unknown

Page Range: Unknown

Description:

Retrofit heat exchanger network (HEN) optimization is a fundamentally unique problem which requires the consideration of existing structures, compared to grassroots design problems. The optimization of retrofit HENs is particularly difficult due to the integration of both existing and newly acquired equipment. The re-routing of existing equipment can lead to various network topologies, increasing the complexity of considerations. In this work, we exploit the P-graph framework to solve retrofit HEN problems, guaranteeing to find the topology of optimal solutions within the constrained space of the HEN retrofit problem. The P-graph framework has additional advantages that allows topologically-efficient search space, simplifies additional unit placement, considers unit positional swapping (re-sequencing and re-piping within search constraints), considers stream splitting, and n-best solution visualization. The pinch minimum utility constraint also provides a bound for the maximum number of modifications in the HEN, significantly reducing search space. The proposed P-graph-based approach is demonstrated using a real refinery case study to show its capability in obtaining the topology of the optimal HEN, highlighting the economic and energy benefits. Further extensions to other retrofit process integration problems (e.g. retrofit water network, hydrogen network etc.) will be enabled via the proposed P-graph approach.

Open Access: Yes

DOI: 10.1016/j.energy.2023.129029

Framework to embed machine learning algorithms in P-graph: Communication from the chemical process perspectives

Publication Name: Chemical Engineering Research and Design

Publication Date: 2022-12-01

Volume: 188

Issue: Unknown

Page Range: 265-270

Description:

P-graph is a popularly used framework for process network synthesis (PNS) and network topological optimization. This short communication introduces a Python interface for P-graph to serve as a linkage to modern programming ecosystems. This allows for a wider application of the fast and efficient P-graph solver, to provide structural and topological enumeration in numerous fields. The proposed framework allows for more integrative usage in Artificial Intelligence (AI), machine learning, process system engineering, chemical engineering and chemometrics. Large and repetitive topologies can also be automated using the new programming interface, saving time and effort in modelling. This short communication serves as a demonstration of the newly developed open-sourced P-graph interface.

Open Access: Yes

DOI: 10.1016/j.cherd.2022.09.043