{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:41:58Z","timestamp":1759797718916,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Computers"],"abstract":"<jats:p>Biofuels represent a sustainable alternative that supports global energy development without compromising environmental balance. This work introduces a novel hardware\u2013software platform for the experimental characterization of biomass solid yield during the slow pyrolysis process, integrating physical experimentation with advanced computational modeling. The hardware consists of a custom-designed pyrolizer equipped with temperature and weight sensors, a dedicated control unit, and a user-friendly interface. On the software side, a two-step kinetic model was implemented and coupled with three optimization algorithms, i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Nelder\u2013Mead (N-M), to estimate the Arrhenius kinetic parameters governing biomass degradation. Slow pyrolysis experiments were performed on wheat straw (WS), pruning waste (PW), and biosolids (BS) at a heating rate of 20 \u00b0C\/min within 250\u2013500 \u00b0C, with a 120 min residence time favoring biochar production. The comparative analysis shows that the N-M method achieved the highest accuracy (100% fit in estimating solid yield), with a convergence time of 4.282 min, while GA converged faster (1.675 min), with a fit of 99.972%, and PSO had the slowest convergence time at 6.409 min and a fit of 99.943%. These results highlight both the versatility of the system and the potential of optimization techniques to provide accurate predictive models of biomass decomposition as a function of time and temperature. Overall, the main contributions of this work are the development of a low-cost, custom MATLAB-based experimental platform and the tailored implementation of optimization algorithms for kinetic parameter estimation across different biomasses, together providing a robust framework for biomass pyrolysis characterization.<\/jats:p>","DOI":"10.3390\/computers14100426","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T13:33:41Z","timestamp":1759757621000},"page":"426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hardware\u2013Software System for Biomass Slow Pyrolysis: Characterization of Solid Yield via Optimization Algorithms"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2725-5930","authenticated-orcid":false,"given":"Ismael","family":"Urbina-Salas","sequence":"first","affiliation":[{"name":"Departamento de Ingenier\u00eda Mec\u00e1nica, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Salamanca 36885, Guanajuato, Mexico"},{"name":"ENAP-Research Group, Departamento de Ingenier\u00eda Mecatr\u00f3nica, Tecnol\u00f3gico Nacional de M\u00e9xico, ITS Guanajuato (ITESG), Carr. Guanajuato a Puentecillas km 10.5, Guanajuato 36262, Guanajuato, Mexico"}]},{"given":"David","family":"Granados-Lieberman","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, Departamento de Ingenier\u00eda Electromec\u00e1nica, Tecnol\u00f3gico Nacional de M\u00e9xico, ITS Irapuato (ITESI), Carr. Irapuato-Silao km 12.5, El Copal, Irapuato 36821, Guanajuato, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9559-0220","authenticated-orcid":false,"given":"Juan Pablo","family":"Amezquita-Sanchez","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos y Control, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo 76807, Quer\u00e9taro, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3839-1396","authenticated-orcid":false,"given":"Martin","family":"Valtierra-Rodriguez","sequence":"additional","affiliation":[{"name":"ENAP-Research Group, CA-Sistemas Din\u00e1micos y Control, Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro (UAQ), Campus San Juan del R\u00edo, R\u00edo Moctezuma 249, Col. San Cayetano, San Juan del R\u00edo 76807, Quer\u00e9taro, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7033-0230","authenticated-orcid":false,"given":"David Aaron","family":"Rodriguez-Alejandro","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Mec\u00e1nica, Universidad de Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8, Salamanca 36885, Guanajuato, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Misbahuddin, M., Cokrowati, N., Iqbal, M.S., Farobie, O., Amrullah, A., and Ernawati, L. (2025). Kalman Filter-Enhanced Data Aggregation in LoRaWAN-Based IoT Framework for Aquaculture Monitoring in Sargassum sp. Cultivation. 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