Juan Carlos Maya

A computational model of anisotropic biomass particle pyrolysis was developed to evaluate the influence of particle properties and operating conditions during fast pyrolysis. The model integrates multicomponent CRECK kinetics with intraparticle heat and mass transfer. Simulations were performed at reactor temperatures between 400 and 700 °C and convective heat transfer coefficients from 50 to 1500 W·m⁻²·K⁻¹, representing conditions from slow to fast pyrolysis. Results showed that particle size and biomass composition strongly affect conversion time and product distribution, while aspect ratio becomes significant for larger particles. Increasing particle diameter from 3 to 8 mm more than doubled conversion times and increased char yield from approximately 16% to 23%. Lignin-rich biomass such as palm shell generated higher char yields and lower volatile production than cellulose-rich feedstocks such as oak or sugarcane bagasse. Higher reactor temperatures and heating rates reduced conversion times by up to 75%, with moderate effects on product yields. Analysis of Biot and pyrolysis numbers indicated that millimeter-scale particles operate in a transition regime where conduction, convection, and chemical kinetics occur on similar timescales. Therefore, accurate reactor design models must simultaneously consider coupled transport and reaction phenomena.

Hydrothermal carbonisation (HTC) is a promising strategy for managing sewage sludge (SS), a wet, heterogeneous, mineral-rich residue with limited direct energy recovery potential. This study evaluated the HTC of high-moisture SS by integrating a rotatable central composite design, experimental validation, physicochemical characterisation of the hydrochar, and a global kinetic model calibrated against measured temperature profiles, T(t). The raw SS exhibited a high ash content, which increased further in the hydrochar, indicating mineral enrichment in the solid product. This behaviour limited energetic upgrading, as the estimated higher heating value decreased slightly after HTC. The enrichment of metals such as Co, Ni, and Hg also indicates that direct soil application should be avoided without prior environmental safety assessment. Temperature was the dominant factor affecting hydrochar yield and carbon efficiency, while the interaction between initial pressure and residence time significantly influenced BET surface area. The kinetic model captured the main product distribution trends, with an overall mean relative error of 11.5% for hydrochar yield and activation energies ranging from 65.8 to 82.5 kJ/mol. HTC should therefore be considered a sludge redesign strategy that transforms a difficult-to-manage waste into a more stable carbonaceous-mineral matrix. This transformation can facilitate SS handling and storage while creating opportunities for alternative, controlled uses, such as contaminant immobilisation, catalytic precursor development, or functional materials. In this sense, HTC can contribute to the circular economy and to Colombia's sustainability goals.

A computational model of anisotropic biomass particle pyrolysis was used to study the influence of particle properties and process conditions. The model couples multicomponent CRECK kinetics with intraparticle heat and mass transport. Particle size and lignocellulosic composition significantly affect conversion time and product yields; aspect ratio was also found to be important for larger-diameter particles. Larger particles (8 mm diameter, 4:1 aspect ratio) showed conversion times more than twice those of 3 mm particles, and char yield increased from about 16% to 23% when comparing small and large particles. Lignin-rich materials (e.g., palm shell) produced higher char and lower volatile yields than cellulose-rich biomass (wood, sugarcane bagasse); for 3 mm particles, char changed from 16% (oak) to 23% (palm shell). Higher reactor temperatures and heating rates substantially shortened particle conversion time—by up to 75%—and noticeably affected product yields. Analysis of the Biot and Pyrolysis numbers indicates millimeter-scale particles operate in a transition regime where internal conduction, external convection, and chemical kinetics occur on comparable timescales, so models must include these phenomena to accurately predict conversion times and final yields for reactor design and optimization.

This study presents an exergy analysis of a low emission hydrogen production process based on the thermal pyrolysis of natural gas. The system was modeled in Aspen Plus with a production capacity of 2.6 and 6.3 tonnes per day of hydrogen and carbon black, respectively. The energy required by the process is supplied through the combustion of 22% of the produced carbon black and the residual gases from pyrolysis after hydrogen separation. The results revealed that the overall exergetic efficiency reaches 89.4% when both hydrogen and carbon black are considered valuable products, but decreases to 52.5% when only hydrogen is considered. This highlights the importance of valorizing carbon black to improve both the exergetic efficiency and the economic viability of the system. Carbon black has established applications in the production of tires, electrical components, printing inks, high-performance coatings, and plastics. The largest exergy destruction occurred in the pyrolysis reactor and the combustor, where chemical reactions and heat transfer contribute significantly to process irreversibilities. The process generates approximately 3.15 kg CO₂/kg H₂, significantly lower than typically emitted by conventional steam methane reforming (SMR). These low emissions qualify the hydrogen produced as low carbon under U.S. and Canadian standards, enabling access to clean hydrogen incentives. These findings position thermal natural gas pyrolysis as a promising alternative for sustainable hydrogen production and as a potential carbon removal strategy when fueled with renewable natural gas.

Biochar has emerged as a promising adsorbent for removing organic pollutants from aqueous media, with its efficiency strongly influenced by the feedstock and pyrolysis conditions. In this study, biochar produced from fique pellets under controlled pyrolysis was evaluated using methylene blue (MB) as a model contaminant. The cation exchange capacity reached up to 17 meq g⁻¹ for biochar obtained at lower temperatures, while those produced at 700 °C showed values below the detection limit, consistent with the depletion of oxygenated functional groups observed in FTIR spectra. Batch adsorption experiments revealed removal efficiencies above 99% for biochar produced at 550 °C and 700 °C (45 min). The 700 °C biochar exhibited faster initial adsorption due to its larger surface area, whereas the 550 °C biochar achieved higher and more stable overall removal over prolonged contact times, attributed to the preservation of surface functional groups and measurable CEC. Kinetic modeling demonstrated that the adsorption process followed the Özer model, indicating heterogeneous surface interactions and diffusion-controlled steps. These results highlight the influence of pyrolysis temperature on adsorption kinetics and support the potential of biochar obtained from fique pellets as a sustainable, low-cost material for water purification and agro-industrial residue valorization.

This study presents a novel semi-physical phenomenological model that addresses a significant gap in the literature by incorporating dynamic 1D mass, momentum, and energy balances across all zones of a single-screw extruder. The model was implemented for a conventional screw geometry with a smooth feed section. The methodology involved spatial discretization using upwind finite difference schemes, resulting in a system of differential-algebraic equations (DAE). This system was solved numerically and validated with experimental data from an industrial extruder processing polypropylene. Simulation results demonstrate that the model accurately predicts plastification profiles, melt temperatures, and pressure distributions with low prediction errors. Mean Absolute Error (MAE) values of 7.45 × 10⁻⁶ m² for the solid flow area, between 0.51 and 8.96 bar for pressure, and 1.66°C for the melt temperature were obtained. A key feature is its computational efficiency, with simulations requiring a maximum time of 875 seconds. As far as we know, this work presents the first dynamic 1D model that unifies all key extrusion zones in a fully coupled manner. The key novelty of this work is that its rapid and accurate predictive capabilities make it a valuable tool for screw geometry optimization, sensitivity studies, and integration into digital-twin frameworks to support decision-making in industrial operations.

This work presents an effective design of a strategy to manage biomass waste (empty fruit bunch—EFB, kernel shell, and fiber) available from the processing of oil palm (Elaeis guineensis) in Colombia as a renewable energy source. This type of study is conducted for the first time in the country, and the proposed strategy is structured in four phases. Firstly, an inventory of available biomass waste was prepared based on information from 45 African palm oil companies of the approximately 70 that exist in the country. It was determined that the country had about 2762 kt of available waste (63.64% EFB, 12.55% kernel shell, and 23.81% fiber) for the year 2023. The estimates were conducted using a model that correlates processing capacity, the biomass generated, and the biomass demanded. The validation was performed using national reports. Subsequently, the minimum number (six) of storage centers in Colombia, where the largest amount of biomass can be stored, was determined. The center of gravity method was used to find the geographical location of each bulk storage center (municipality of Aracataca, Agustín Codazzi, San Martín, Puerto Wilches, Castilla La Nueva, and Cabuyaru). The next step was to determine the transportation costs as a decision criterion to select the best bulk storage center. When the required storage capacity does not exceed 211 kt·year⁻¹, Agustín Codazzi is the best option because it has the lowest transportation cost (USD 1.01·t⁻¹). When the storage capacity requirements exceed 211 kt·year⁻¹ but are less than 423 kt·year⁻¹, then Puerto Wilches and/or Aracataca are the best options (transportation cost of USD 1.7·t⁻¹). In all cases, Cabuyaru has the highest costs (USD 6.56·t⁻¹). Finally, an energy potential of 50,196 × 10⁶ GJ·year⁻¹ for the collected biomass was estimated, which makes this kind of waste an environmental alternative that could replace coal in Colombia.

Artificial Neural Networks (ANNs) have been successfully applied to predict various parameters in extrusion processes. However, few studies were found that have demonstrated their use in predicting the pressure profile along a single-screw extruder, and in these studies, motor current demand and melt temperature were not used as input variables. In this study, Feedforward Artificial Neural Networks (FANNs) were applied to predict the pressure profile along a single-screw extruder. The input layer variables tested included motor current demand, melt temperature, screw speed, and die restriction, while the output layer variables evaluated were four pressure measurements at different zones of the extruder. Motor current demand explained most of the data variability, accounting for 97.1% of the total variability. Five FANN architectures were tested: two considered all four input variables, two excluded the melt temperature, and the last one included only motor current demand and screw speed. The most accurate model included all four input variables, achieving a mean squared error (MSE) of 0.0078 and a coefficient of determination (R2) of 0.9889 with the test data. The variation between models including or excluding melt temperature was only in the fourth decimal place for R2. The FANN model proved efficient in predicting the pressure profile in single-screw extruders. These results provide valuable insights for improving extruder control systems and reducing costs associated with replacing defective sensors.