Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes

Heterogeneous, multi-component materials such as industrial tailings or by-products, along with naturally occurring materials, such as ores, have been intensively investigated as candidate oxygen carriers for chemical-looping processes. However, these materials have highly variable compositions, and this strongly influences their chemical-looping performance. Here, using machine learning techniques, we estimate the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping. Experimental data for 19 manganese ores chosen as potential chemical-looping oxygen carriers were used to create a so-called training database. This database has been used to train several supervised artificial neural network models (ANN), which were used to predict the reactivity of the oxygen carriers with different fuels and the oxygen transfer capacity with only the knowledge of reactor bed temperature, elemental composition, and mechanical properties of the manganese ores. This novel approach explores ways of dealing with the training dataset, learning algorithms and topology of ANN models to achieve enhanced prediction precision. Stacked neural networks with a bootstrap resampling technique have been applied to achieve high precision and robustness on new input data, and the confidence intervals were used to assess the precision of these predictions. The current results indicate that the best trained ANNs can produce highly accurate predictions for both the training database and the unseen data with the high coefficient of determination (R2 = 0.94) and low mean absolute error (MAE = 0.057). We envision that the application of these ANNs and other machine learning algorithms will accelerate the development of oxygen carrying materials for a range of chemical-looping applications and offer a rapid screening tool for new potential oxygen carriers

A New Approach to Environmental Valuation for New Zealand

New Zealand’s Resource Management Act is frequently criticised for the costs and delays it imposes on activities, but less attention is given to the consistency of values it applies to environmental effects through its decisions. The wide variety of parties who exercise decision roles under the act lack guidance on the economic value of the environment, and non-market valuation studies are too costly to be widely used and too few and varied to infer reliable generic values. Drawing on experience in estimating the public value of safety improvements, this article proposes an alternative approach that measures people’s aversion to the risk of environmental impacts of different scales and severity which could yield values sufficiently generic to be widely used, and outlines its uses both within and beyond the RMA applications

The extent of sorbent attrition and degradation of ethanol-treated CaO sorbents for CO2 capture within a fluidised bed reactor

The application of an ethanol pre-treatment step on biomass-templated calcium looping sorbents resulting in an improved pore structure for cyclic CO2 capture was investigated. Three ethanol solutions of varying concentrations were used with an improved pore and particle structure, and thermogravimetric analyser CO2 carrying capacity arising with the 70 vol% ethanol solution. The extent of attrition of these sorbents was tested within a fluidised bed reactor and compared against an untreated sorbent and a limestone base case. It found that despite the ethanol-treated sorbents displaying an admirable CO2 carrying capacity within the thermogravimetric analyser even under realistic post-combustion conditions, this was not translated equivalently in the fluidised bed. Attrition and elutriation of the biomass-templated sorbents was a significant issue and the ethanol pre-treatment step appeared to worsen the situation due to the roughened surface and mechanically weaker structure

Hydrogen production by sorption enhanced steam reforming (SESR) of biomass in a fluidised-bed reactor using combined multifunctional particles

The performance of combined CO2-sorbent/catalyst particles for sorption enhanced steam reforming (SESR), prepared via a simple mechanical mixing protocol, was studied using a spout-fluidised bed reactor capable of continuous solid fuel (biomass) feeding. The influence of particle size (300–500 and 710–1000 µm), CaO loading (60–100 wt %), Ni-loading (10–40 wt %) and presence of dicalcium silicate support (22.6 wt %) on SESR process performance were investigated. The combined particles were characterised by their density, porosity and CO2 carrying capacity with the analysis by thermogravimetric analysis (TGA), Brunauer-Emmett-Teller (BET), Barrett-Joyner-Halenda (BJH) and mercury intrusion porosimetry (MIP). All experiments were conducted with continuous oak biomass feeding at a rate of 0.9 g/min ± 10%, and the reactor was operated at 660 ± 5 °C, 1 atm and 20 ± 2 vol % steam which corresponds to a steam-to-carbon ratio of 1.2:1. Unsupported combined particles containing 21.0 wt % Ni and 79 wt % CaO were the best performing sorbent/catalyst particle screened in this study, when accounting for the cost of Ni and the improvement in H2 produced by high Ni content particles. SESR tests with these combined particles produced 61 mmol H2/gbiomass (122 g H2/kgbiomass) at a purity of 61 vol %. Significant coke formation within the feeding tube and on the surfaces of the particles was observed which was attributed to the low steam to carbon ratio utilised

Design and performance testing of a monolithic nickel-based SiC catalyst for steam methane reforming

Hydrogen is a highly promoted carbon-free energy carrier that has drawn significant attention recently due to its potential to decarbonise energy sector. More than three-quarters of hydrogen is currently produced via steam methane reforming (SMR), and nickel-based catalysts are used in most applications. Structured catalysts have been reported to be able to further improve catalyst performance as they can optimise heat and mass transfer, as well as prevent coke formation with its structural and textural proprieties. Silicon carbide (SiC) has excellent hardness, thermal conductivity, and chemical inertness, therefore is a promising material to develop structured nickel-based monolithic SiC catalysts for SMR. In this work, a structured monolithic catalyst support has been formed by a modified freeze-gelation method, initially starting from SiC powder, and nickel has been distributed to form a monolithic nickel-based catalyst by wet impregnation. The results showed that the catalysts can achieve thermodynamic equilibrium at 600 °C with a gas hourly space velocity (GHSV) of 10,000 h−1, while reaching a high methane conversion of 86% at 800 °C and GHSV value of 20,000 h−1 during the performance tests using low feeding concentration and low pressure. This is the first time SiC catalytic materials have had their performance demonstrated for SMR under realistic operating conditions

Investigation of the apparent kinetics of air and oxy-fuel biomass combustion in a spouted fluidised-bed reactor

A bench-scale spouted fluidised-bed reactor was used to investigate the combustion kinetics of pulverised woody biomass under air and oxy-fuel atmospheres. Bed temperatures were in the range of 923-1073 K and O2 concentrations were varied from 20-35 vol%. The activation energies and apparent orders of reaction were calculated for air and oxy-fuel combustion by means of an nth order Arrhenius equation approach. Results indicated that the apparent order of reaction for both air and oxy-fuel combustion was approximately zero. The activation energies were calculated assuming a zero-order reaction mechanism and were averaged over all oxygen concentrations for air and oxy-fuel combustion and found to be 18.95 kJ/mol and 26.93 kJ/mol, respectively. The rate of combustion under oxy-fuel conditions was, on average, 37.5% higher compared to air combustion. The shrinking core model with a reaction-controlled step was found to accurately represent the biomass combustion reactions under both air and oxy-fuel conditions

Prediction of combined sorbent and catalyst materials (CSCM) for SE-SMR, using QSPR and multi-task learning

The process of sorption enhanced steam methane reforming (SE-SMR) is an emerging technology for the production of low carbon hydrogen. The development of a suitable catalytic material, as well as a CO2 adsorbent with high capture capacity, has slowed the upscaling of this process to date. In this study, to aid the development of a combined sorbent catalyst material (CSCM) for SE-SMR, a novel approach involving quantitative structure–property relationship analysis (QSPR) has been proposed. Through data-mining, two databases have been developed for the prediction of the last cycle capacity (gCO2/gsorbent) and methane conversion (%). Multitask learning (MTL) was applied for the prediction of CSCM properties. Patterns in the data of this study have also yielded further insights; colored scatter plots were able to show certain patterns in the input data, as well as suggestions on how to develop an optimal material. With the results from the actual vs predicted plots collated, raw materials and synthesis conditions were proposed that could lead to the development of a CSCM that has good performance with respect to both the last cycle capacity and the methane conversion

Activated carbon derived from biomass combustion bottom ash as solid sorbent for CO2 adsorption

Climate change and global warming, caused mainly by the anthropogenic CO2 emissions, has been recognised to be the biggest threat to global ecosystems. Replacing fossil fuels with sustainable biomass for heat and power generation is a key tool in our fight against climate change. Such combustion, however, generates large quantities of ash which, unlike the coal counterparts, are yet to find major applications in industry. This leads to challenging waste management and thus, necessitating urgent measures to valorise this increasing waste stream. However, producing activated carbon from biomass combustion ash allows for not only effective waste valorisation into value-added products, but also to prepare a sorbent for post-combustion carbon capture from an abundant and cheap source that is readily available for in-situ application (hence, minimising overall costs). This work has focused on preparation and activation of industrial-grade biomass ash-derived porous carbon via an economical direct method, followed by an extensive characterisation of its textural properties as well as an evaluation of the CO2 uptake of both the virgin and the activated carbonaceous sorbents. The final sample was selected based on an extensive optimisation campaign aiming towards maximisation of yield and CO2 uptake. The optimum activated sample adsorbed 0.69 mmol/g, thus, nearly doubling the adsorption capacity of the virgin biomass combustion bottom ash-derived carbon.Engineering and Physical Sciences Research Council (EPSRC): EP/W002841/

High-throughput screening of sulfur-resistant catalysts for steam methane reforming using machine learning and microkinetic modeling

The catalytic activity of bimetallic catalysts for the steam methane reforming (SMR) reaction was extensively studied previously. However, the performance of these materials in the presence of sulfur-containing species is yet to be investigated. In this study, we propose a novel process aided by machine learning (ML) and microkinetic modeling for the rapid screening of sulfur-resistant bimetallic catalysts. First, various ML models were developed to predict atomic adsorption energies (C, H, O, and S) on bimetallic surfaces. Easily accessible physical and chemical properties of the metals and adsorbates were used as input features. The Ensemble learning, artificial neural network, and support vector regression models achieved the best performance with R2 values of 0.74, 0.71, and 0.70, respectively. A microkinetic model was then built based on the elementary steps of the SMR reaction. Finally, the microkinetic model, together with the atomic adsorption energies predicted by the Ensemble model, were used to screen over 500 bimetallic materials. Four Ge-based alloys (Ge3Cu1, Ge3Ni1, Ge3Co1, and Ge3Fe1) and the Ni3Cu1 alloy were identified as promising and cost-effective sulfur-resistant catalysts

Synthesis of highly effective stabilized-CaO sorbents via a sacrificial N-doped carbon nanosheet template

Calcium looping, a promising high-temperature CO2 capture technique, offers a midterm economic solution to mitigate anthropogenic CO2 emissions. The main challenge for calcium looping is the synthesis of highly efficient CaO-based sorbents that can be used over many reaction cycles. Here, a sacrificial N-doped carbon nanosheet template was developed which produces MgO-stabilized, CaO sorbents with fast adsorption rates, high capacities and remarkable long term performance over many cycles. The characterization results show that such a template was formed through in situ pyrolysis of an organic acid and nitrates in a simple heating process under nitrogen. The presence of a carbonaceous template prevented crystallite growth, featured highly macroporous nanosheet (~60 nm thick) morphologies, ensured homogeneously mixing of Ca and Mg, which is essential to attain minimal diffusion limitations, mitigated sintering, and produced structural stabilization. Thus, 10 mol% MgO acting as an inert stabilizer was sufficient to achieve a CO2 uptake of 0.65 g/g (corresponding to a capacity retention of 89.9%) after 10 cycles in realistic conditions, as confirmed by TGA analyses. This N-doped carbon template can be applied generally to form a wide range of porous and nanostructured stabilized-CaO sorbents with stable CO2 uptakes