In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R²) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogen-content are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the sm_hydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the sm_hydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on sm_hydrogen.

This work aims to explain aluminum hydrolysis reaction kinetics based on a properly chosen theoretical model with machined aluminum waste chips as well as alkali solutions up to 1M as a promoter and to estimate the overall reaction profit. The purpose of this work is to assess the optimal alkali concentration in the production of small- and medium-scale green hydrogen. To obtain results with better accuracy, we worked with flat Al waste chips, because a flat surface is preferable to maximally increase the time for the created hydrogen bubbles to reach the critical gas pressure. Describing the reaction kinetics, a flat shape allows for the use of a planar one-dimensional shrinking core model instead of a much more complicated polydisperse spheric shrinking core model. We analyzed the surface chemical reaction and mass transfer rate steps to obtain the first-order rate constant for the surface reaction and the diffusion coefficient of the aqueous reactant in the byproduct layer, respectively. We noted that measurements of the diffusion coefficient in the byproduct layer performed and discussed in this paper are rare to find in publications at alkali concentrations below 1M. With our reactor, we achieved a H₂ yield of 1145 mL per 1 g of Al with 1M NaOH, which is 92% of the theoretical maximum. In the estimation of profit, the authors’ novelty is in paying great attention to the loss in alkali and finding a crucial dependence on its price. Nevertheless, in terms of consumed and originated materials for sale, the conversion of aluminum waste material into green hydrogen with properly chosen reaction parameters has positive profit even when consuming an alkali of a chemical grade.

This study presents a new simulation model developed with ASPEN Plus of waste biomass gasification integrated with syngas fermentation and product recovery units for bioethanol production from garden waste as a lignocellulosic biomass. The simulation model includes three modules: gasification, fermentation, and ethanol recovery. A parametric analysis is carried out to investigate the effect of gasification temperature (500–1500 °C) and equivalence ratio (0.2–0.6) on the gasification performance and bioethanol production yield. The results reveal that, for efficient gasification and high ethanol production, the operating temperature range should be 700–1000 °C, as well as an equivalence ratio between 0.2 and 0.4. At optimal operating conditions, the bioethanol production yield is 0.114 kg/h per 1 kg/h input garden waste with 50% moisture content. It is worth mentioning that this parameter increases to 0.217 kg_bioethanol/kg_{garden waste} under dry-based conditions.

The amount of PCDD/F emissions produced by gasification operations is often within standard limits set by national and international laws (<0.1 ng TEQ/Nm³). However, a recent assessment of the literature indicates that gasification cannot always reduce PCDD/Fs emissions to acceptable levels, and thus a common belief on the replacement of incineration with gasification in order to reduce PCDD/Fs emissions seems overly simplistic. A review that summarizes the evidence on when gasification would likely result in environmentally benign emissions with PCDD/F below legal limits, and when not, would be of scientific and practical interest. Moreover, there are no reviews on dioxin formation in gasification. This review discusses the available data on the levels of dioxins formed by gasifying different waste streams, such as municipal solid wastes, plastics, wood waste, animal manure, and sewage sludge, from the existing experimental work. The PCDD/Fs formation in gasification and the operational parameters that can be controlled during the process to minimize PCDD/Fs formation are reviewed.