IPSF: Intelligent Process Simulation Framework for Wolffia globosa Fast Pyrolysis

About

This repository contains the MATLAB implementation and Aspen Plus model for the Intelligent Process Simulation Framework (IPSF) — a hybrid simulation–optimization architecture that couples a validated Aspen Plus steady-state process model directly with the Aquila Optimizer (AO) through automated MATLAB–COM integration. The framework is demonstrated on the multi-objective eco-efficient optimization of fast pyrolysis of Wolffia globosa biomass (an aquatic plant sourced from wastewater phytoremediation systems) for bio-oil production.

Associated Publication

Piyachon, A., Aungkulanon, P., Prasartkaew, B., & Sukpancharoen, S. (2026). Intelligent Process Simulation Framework for Bio-oil Production from Wolffia globosa Fast Pyrolysis via Aquila Optimizer. Energy Nexus, 100713. https://doi.org/10.1016/j.nexus.2026.100713

Open Access under Creative Commons license — freely available at ScienceDirect.

Highlights

• IPSF links Aspen Plus directly with Aquila Optimizer via MATLAB–COM.
• AO improves composite desirability by 3.7–10.5% over RSM-BBD.
• Under 1.8% bio-oil sacrifice reduces CO₂ and electricity cost jointly.
• AO converges within 6–8 iterations across 3- to 5-factor designs.
• Modular framework adapts to other feedstocks via kinetic reconfiguration.

Repository Structure

IPSF/
├── main.m              # Main entry point — runs the IPSF optimization workflow
├── AO.m                # Aquila Optimizer (AO) algorithm implementation
├── AO_Objective.m      # Objective function — sends decision variables to Aspen Plus,
│                       #   retrieves responses, computes composite desirability D
├── initAspen.m         # Initializes Aspen Plus via COM (actxserver) — loads wolffia.bkp
├── initialization.m    # Initial population sampling within decision-variable bounds
├── wolffia.bkp         # Aspen Plus V12.1 process model for W. globosa fast pyrolysis
└── README.md           # This file

File	Type	Purpose
main.m	MATLAB script	Top-level driver: configures the problem (3-, 4-, or 5-factor), launches AO, and reports the optimum
AO.m	MATLAB function	Implements the four hunting-inspired phases of the Aquila Optimizer (high soar, contour flight, low flight, walk-and-grab) following Abualigah et al. (2021)
AO_Objective.m	MATLAB function	Evaluates the composite desirability D by sending decision variables to Aspen Plus and retrieving bio-oil mass flow, CO₂e, and electricity cost
initAspen.m	MATLAB function	Establishes the COM connection to Aspen Plus and loads wolffia.bkp as a background process
initialization.m	MATLAB function	Generates the initial population within the decision-variable bounds (LB, UB)
wolffia.bkp	Aspen Plus model	High-fidelity steady-state process model of W. globosa fast pyrolysis at 100 t/day, including RYield → RCSTR reactor and multi-stage condensation

Software Requirements

Software	Purpose	Notes
MATLAB (R2024a or compatible)	Optimization & COM client	Required to run main.m and the AO algorithm
Aspen Plus (V12.1)	Process simulation engine	Required to open and execute wolffia.bkp. Communicates with MATLAB via COM (actxserver)
Windows OS	COM automation	Aspen Plus COM interface is Windows-only

Usage

Download

git clone https://github.com/sombsuk/IPSF.git
cd IPSF

Recommended Workflow

1. Open Aspen Plus V12.1 and ensure that wolffia.bkp is in the working directory.
2. Launch MATLAB and add the IPSF folder to the path.
3. Open main.m and configure:
   • Number of decision variables (3, 4, or 5)
   • Decision-variable bounds (LB, UB)
   • AO parameters (population N, max iterations T, exploitation factors α, δ, Lévy index β)
4. Run main.m. The script will:
   • Call initAspen.m to launch Aspen Plus via COM and load wolffia.bkp
   • Generate an initial population using initialization.m
   • Iteratively query Aspen Plus through AO_Objective.m and update solutions via AO.m
   • Return the best operating point and its composite desirability D

Decision Variables (5-factor design)

Variable	Symbol	Unit	Bounds
Pyrolysis temperature	Tpyro	°C	450 – 600
Vapor residence time	τ	s	0.5 – 2.0
N₂ carrier-gas flow rate	ṁN₂	kg/h	500 – 2000
Separator pressure	Psep	bar	0.5 – 2.0
Separator temperature	Tsep	°C	100 – 150

Objective Function

The composite desirability index is the unweighted geometric mean of three individual desirabilities (Derringer & Suich, 1980):

$$ D = (d_{oil} \times d_{CO_2e} \times d_{cost})^{1/3} $$

where the three components correspond to maximizing bio-oil mass flow, minimizing CO₂-equivalent emissions, and minimizing electricity cost, respectively. Full mathematical details and parameter values are provided in the associated publication.

Citation

If you use this code or model in your research, please cite the associated publication:

BibTeX:

@article{Piyachon2026ipsf,
  title   = {Intelligent Process Simulation Framework for Bio-oil Production from {Wolffia globosa} Fast Pyrolysis via Aquila Optimizer},
  author  = {Piyachon, Attaphon and Aungkulanon, Pasura and Prasartkaew, Boonrit and Sukpancharoen, Somboon},
  journal = {Energy Nexus},
  pages   = {100713},
  year    = {2026},
  doi     = {10.1016/j.nexus.2026.100713},
  url     = {https://doi.org/10.1016/j.nexus.2026.100713}
}

Authors and Affiliations

Author	Role	Affiliation
Attaphon Piyachon	First author	(1) Center for Alternative Energy Research and Development (CAERD), Khon Kaen University, Thailand
Pasura Aungkulanon	Co-author	(2) Department of Materials Handling and Logistics Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok (KMUTNB), Thailand
Boonrit Prasartkaew	Co-author	(3) Combustion and Solar Energy Laboratory (CASE Lab.), Mechanical Engineering Department, Faculty of Engineering, Rajamangala University of Technology Thanyaburi (RMUTT), Thailand
Somboon Sukpancharoen	Corresponding author	(1) CAERD, Khon Kaen University & (4) Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Thailand

License

This repository is released under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license, consistent with the open-access publication. This means you may:

• ✅ Share — copy and redistribute the material in any medium or format
• ✅ Attribute — give appropriate credit to the authors and cite the publication

But you may not:

• ❌ NonCommercial — use the material for commercial purposes without permission
• ❌ NoDerivatives — distribute modified versions of the material

For full license terms, see CC BY-NC-ND 4.0.

Contact

For questions, collaborations, or technical inquiries:

Assoc. Prof. Somboon Sukpancharoen, Ph.D. (Corresponding Author & Repository Maintainer) Center for Alternative Energy Research and Development (CAERD) Department of Agricultural Engineering, Faculty of Engineering Khon Kaen University, Khon Kaen 40002, Thailand 📧 sombsuk@kku.ac.th