Energy optimization from a binary mixture of non-edible oilseeds pyrolysis: Kinetic triplets analysis using Thermogravimetric Analyser and prediction modeling by Artificial Neural Network

Comprehensive Understanding of the Heavy Oil Combustion Process: Reaction Behavior, Kinetic Triplet, and Mechanism Implication

In this work, thermo-oxidative behavior, kinetic triplet, and free radical mechanism of ultraheavy oil during an in situ combustion (ISC) process were systematically surveyed via multiple thermal analysis techniques (TG/DTG/DSC/PDSC), model-free methods, and related mathematical simulation. First, specific mass loss, exothermic intensity, and corresponding temperature intervals were respectively determined in low-/high-temperature oxidation (LTO/HTO) regions. In addition, the comparison of atmospheric/pressurized differential scanning calorimetry (DSC/PDSC) experiments indicated that the pressurized conditions could obviously strengthen the oxidation progress with more heat emission. Then two model-free methods were contrastively employed for PDSC data to calculate LTO and HTO activation energy variations with the conversion rate. Moreover, the acceleratory rate model for LTO and the Sestak-Berggren model for HTO were accordingly picked as the most probable mechanism functions, which were later used to determine the simulated curves. Then, the simulations of α-T and dα/dT-T curves were respectively attained using Friedman equation in MATLAB software and contrasted with experimental data to validate the accuracy of the yielded kinetic triplet and forecast the combustion behavior. Further, the evolution pathways of the underlying oxidation mechanism was illustrated. This study updates the understanding of the nonisothermal combustion process, contributing to the subsequent numerical simulation and feasible investigation for in situ combustion implementation to enhance heavy oil recovery.

Properties of Mixed Crystal Coprecipitation Substances of Ammonium Nitrate and Potassium Perchlorate Prepared by the Evaporative Solvent Method

Ammonium perchlorate (AP) has been widely used as an oxidizer in propellants and military mixed explosives in recent years. However, its high characteristic signal, environmental pollution, and poor detonation performance have prompted the industry to seek alternatives to AP. Ammonium nitrate (AN) is a suitable substitute due to its low characteristic signal, lack of pollution, and excellent detonation performance. However, its room-temperature phase transition and hygroscopicity affect its practical use. In this work, we prepared mixed crystal coprecipitation (MCC) materials of AN and potassium perchlorate (KP) using the evaporative solvent method. The characterization of AN/KP MCCs was carried out by combining TG-DSC, XRD, FT-IR, and SEM analysis, revealing that the formation of MCCs by AN and KP is due to ion exchange between the two components. AN/KP MCCs not only solve the problem of room-temperature phase transition in AN but also reduce its hygroscopicity. Furthermore, AN/KP MCCs have mechanical sensitivity, explosive performance, and specific impulse similar to pure AN, but compared to AN, AN/KP MCCs have higher density, effective oxygen content, and thermal stability. Compared with existing oxidizers AN, AP, and KP, AN/KP MCCs with high density, low sensitivity, high oxygen content, and high safety have obvious advantages and have good prospects in the application of oxidizers in solid propellants and military mixed explosives.

Advanced dual-artificial neural network system for biomass combustion analysis and emission minimization

Management of agricultural waste is an important part of plantation operations. Not all wastes are suitable for composting or the process is simply inefficient and time-consuming. In their case, thermal treatment is acceptable, but it is necessary to optimize the process to minimize greenhouse gas emissions. This article investigates the feasibility of constructing artificial neural networks (ANNs) to predict feedstock and emission parameters from the combustion of vineyard biomass. In order to maximize accuracy while avoiding overfitting of the ANN, a novel dual-ANN system was proposed. It consisted of two cascade-forward ANNs trained on independent data, each with three hidden layers. A benchmark showed that the final networks had a relative error in the range of 0.81-2.83%, and the resulting dual-ANN up to a maximum of 2.09%. Based on the ANN, it was possible to make recommendations on the parameters of the feedstock that would be suitable for obtaining bioenergy. It was noted that the best calorific values are shown by waste from plants with an intermediate amount, distribution, and mass of leaves, with relatively low average leaf area. Emissivity reduction, however, requires significantly different conditions. Preference is given to waste from plants that have high amounts of leaves but are spread over many stems - that is, plants that are highly shrubby during the growing season. This proves that it is not possible to formulate universal recommendations that are both energy- and carbon-beneficial, but outlines a clear direction where consensus should be sought, depending on the goals adopted.

New trends on green energy and environmental technologies, with special focus on biomass valorization, water and waste recycling: editorial of the special issue

In this editorial piece, the Editors of the Virtual Special Issue (VSI) "New Trends on Green Energy and Environmental Technologies, with Special Focus on Biomass Valorization, Water and Waste Recycling", present summarized data corresponding to the accepted submissions, as well as additional comments regarding the thematic of the VSI. Overall, 83 manuscripts were received, with final publication of those having the highest quality, accepted after peer-reviewing. The Editors think that the result is a set of very interesting papers that increase the knowledge on the matter, and which would be useful for researchers and the whole society.

Insights on kinetic triplets and thermodynamic analysis of Delonix regia biomass pyrolysis

The aim of this work was to study the pyrolysis of Delonix regia biomass with non-isothermal thermogravimetric experiments. The targeted objective was to investigate kinetic triplets and thermodynamic parameters. Five iso-conversional methods, namely Differential Friedman, Kissinger-Akahira-Sunose, Ozawa-Flynn-Wall, Starink, and Distributed Activation Energy, have been considered. In the adopted heating rates of 5-55 °C min^-1, the average activation energy and pre-exponential factor varied in the range 202.34-205.89 kJ mol^-1 and 4.98 × 10¹⁷ - 2.04 × 10²⁰ s^-1 respectively. Corresponding average enthalpy and Gibbs free energy varied from 196.84 to 200.87 kJ mol^-1 and from 182.64 to 206.41 kJ mol^-1 respectively. Pyrolysis mechanism have been confirmed by Avrami-Erofeyev (A4), power-law (P2 and P4) and reaction (F1, F2, and ≥ F5) according to Criado's master plots.

Plastic regulates its co-pyrolysis process with biomass: Influencing factors, model calculations, and mechanisms

Co-pyrolysis of plastics and biomass can effectively improve the quality of bio-oil and solve the problem of plastic pollution. However, synergistic effect of co-pyrolysis on kinetics and the role of biomass H/Ceff in co-pyrolysis are still not conclusive. In this work, the co-pyrolysis synergistic effects of three different hydrogen-to-carbon ratio (H/Ceff) of biomass-rice husk (RH), sugarcane bagasse (SUG), and poplar wood (PW) with hydrogen-rich polypropylene (PP) were studied using a thermogravimetric method. The total synergy degree (φ) and the difference between experimental and theoretical weight losses (ΔW) were defined, and the activation energies of various experimental materials were calculated by the isoconversional method. The results showed that the addition of PP reduced the dependence of product species on biomass H/Ceff during co-pyrolysis. The synergistic effect of biomass and PP was related to biomass types, pyrolysis temperature, and mass ratio of biomass to PP. The mixture of SUG and PP showed positive synergistic effect at all mass ratios. Simultaneously, at the low temperature of pyrolysis, the synergistic effect is inhibited in all mixtures, which might be due to the melting of PP. Kinetic analysis showed that the activation energy could be reduced by 11.14–31.78% by co-pyrolysis with biomass and PP. A multi-step mechanism was observed in both the pyrolysis of a single sample and the co-pyrolysis of a mixture, according to Criado’s schematic analysis.