Bridging The Analytical Gap: A Computational Tool On The Web For Rapid Lignocellulosic Assessment In Green Fuel Production Systems

Lignocellulosic biomass is a cornerstone of sustainable energy, yet, determining the precise proportions of its primary components (cellulose, hemicellulose, and lignin) remains a major industrial bottleneck. Conventional wet chemistry methods are labor-intensive and slow, creating an "analytical gap" that hinders real-time process control in biorefineries. This paper presents a rapid computational tool accessible via the Web to bridge this gap. The tool utilizes data from Thermogravimetric Analysis (TGA) and applies curve fitting to the differential thermogravimetry (DTG, rate of thermal degradation) signal to estimate biomass composition. By fitting experimental curves to a sum of Gaussian- or Weibull-type functions representing four chemical pseudo-components, the tool resolves a 12-variable optimization problem. The method was tested using data of Arundo donax L. (cane) biomass, showing all concordance between computed lignocellulosic composition and data from the literature obtained by classic wet chemistry methods. Evaluation of various Python-based optimization algorithms identified the Sequential Least Squares Programming (SLSQP) method as the most efficient for this purpose. The public web interface, built with a Python back-end and PHP, offers a verifiable and cost-effective alternative to traditional laboratory protocols, facilitating enhanced cooperation between academia and industry.