Technical evaluation of plasma application in a biorefinery using a mixture of lignocellulosic biomass and municipal waste employing a nonlinear modeling approach

Articles in Press

Document Type : Research article

Authors

1 Tehran, Iran Tehran, Iran

2 Biosystem department, Agricultural Research Institute, Iranian Research Organization for science and Technology

3 General Office of Information Technology, Iranian Research Organization for Science and Technology

4 Department of Biotechnology Research Organization for Science and Technology, Tehran

10.22128/ansne.2026.3337.1222

Abstract

Biorefineries, with the capacity to process highly heterogeneous mixtures of lignocellulosic biomass and municipal solid waste into diverse bioproducts, play a crucial role in advancing a circular green economy. However, first generation of these refineries, produced using traditional methods, were not very successful on an industrial scale due to problems such as the inability of green products to compete economically with petroleum products, the generation of toxic inhibitors during processing, and high sensitivity to fluctuations in feedstock composition and moisture. To address these challenges, a comprehensive simulation was conducted in a Python environment to manage feedstock heterogeneity using a Nonlinear Model Predictive Control (NMPC) architecture, integrated with a sensor fusion strategy that combines Near-Infrared (NIR) spectroscopy, Red-Green-Blue (RGB) optics, and Convolutional Neural Networks (CNNs). Simulation results demonstrated that the proposed model identified and classified over 98% of the heterogeneous input feed with exceptional accuracy. Furthermore, the NMPC controller outperformed conventional Proportional-Integral-Derivative (PID) systems in managing dynamic moisture disturbances by eliminating dampening oscillations and preventing reactor thermal shocks. Findings indicate that implementing plasma technology addresses critical production bottlenecks in biorefineries through shortened production times, enhanced efficiency, decreased raw material consumption, and a reduction in tar concentration of over 82.9%. In lignocellulosic processing, the application of highly selective mechanical attacks on amorphous cellulose increased the yield of Crystalline Nanocellulose (CNC) to unprecedented levels.

Keywords

Main Subjects

Analytical and Numerical Solutions for Nonlinear Equations

Articles in Press, Accepted Manuscript
Available Online from 02 November 2026

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History

  • Receive Date: 02 June 2026
  • Revise Date: 16 September 2026
  • Accept Date: 13 October 2026
  • Publish Date: 02 November 2026

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