The absorption and desorption of carbon dioxide in aqueous monoethanolamine (MEA) was measured in a rotating packed bed of size 398 mm outside diameter, 156 mm inside diameter, and axial depth 25 mm. The effect of lean amine temperature (20 and 40°C), peripheral rotor gravity (31 and 87 g), and various MEA concentrations were investigated. Using MEA concentrations above 30 wt % achieved lower CO 2 penetration levels. This is particularly pronounced for the 100% MEA solution. Comparison with conventional columns showed the advantages of using rotating packed beds in terms of saving size and space and efficient operation.

The state-of-the-art technology to capture CO2 from coal-fired power plants is absorption/stripping with aqueous monoethanolamine (MEA). The energy consumption in stripping can be 15−30% of the power-plant output. A rigorous rate-based model for CO2−MEA−H2O was used to simulate several flowsheet alternatives that reduce the energy requirement using Aspen Plus with RateFrac. Results were calculated for vapor recompression, multipressure, and simple strippers at 5 and 10 °C approach temperatures and 70, 90, and 95% CO2 removal. The “equivalent work of steam/mole of CO2 removed” and the reboiler duty were used to compare the proposed schemes and to show the shift of energy use from work to heat. The total equivalent work for multipressure was less than that for the simple stripper by 0.03−0.12 GJ/(ton of CO2), and the reboiler duty was less by 0.15−0.41 GJ/(ton of CO2). The multipressure with vapor recompression is an attractive option because it utilizes the overhead water vapor latent heat to reduce reboiler duty load, recovers the work of compression to strip more CO2, and shows more reversible behavior.

ABSTRACT:The results are presented for a detailed investigation involving the free-radical photopolymerization of n-butyl acrylate in the form of thin static films. The aim of this work is to benchmark the performance of a novel thin film spinning disk reactor that may be used for the continuous production of linear polymers using photoinitiation. Industrially relevant film thicknesses (200 m to 1 mm) are studied as opposed to earlier work that looked into extremely thin films (5-25 m). Such extreme film thicknesses will be difficult to sustain in a thin film reactor without adversely affecting the wettability of the reaction surface and the uniformity of the film. The effects of four main variables (film thickness, UV intensity, initiator concentration, and exposure time) are studied under static film conditions. A 366-nm wavelength is utilized for the UV radiation with 2,2-dimethoxy-2-phenylacetophenone (Irgacure 651) as the photoinitiator dissolved in n-butyl acrylate. The molecular weights, polydispersities, and monomer conversions are measured by gel permeation chromatography. In a 400 m thick film, conversions of Ͼ90% can be achieved with an exposure time of 40 s at a radiation intensity of 175 mW/cm 2 . The results using the same polymerization system in the spinning disk reactor are presented and compared with the static film results in Part II of this series.

The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter.

Reliable prediction of municipal solid waste (MSW) generation rates is a significant element of planning and implementation of sustainable solid waste management strategies. In this study, the multi-layer perceptron artificial neural network (MLP-ANN) is applied to verify the prediction of annual generation rates of domestic, commercial and construction and demolition (C&D) wastes from the year 1997 to 2016 in Askar Landfill site in the Kingdom of Bahrain. The proposed robust predictive models incorporated selected explanatory variables to reflect the influence of social, demographical, economic, geographical and touristic factors upon waste generation rates (WGRs). The Mean Squared Error (MSE) and coefficient of determination ( R2) are used as performance indicators to evaluate effectiveness of the developed models. MLP-ANN models exhibited strong accuracy in predictions with high R2 and low MSE values. The R2 values for domestic, commercial and C&D wastes are 0.95, 0.99 and 0.91, respectively. Our results show that the developed MLP-ANN models are effective for the prediction of WGRs from different sources and could be considered as a cost-effective approach for planning integrated MSW management systems.

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Sustainable solid waste management can provide pathways for renewable energy generation. The Kingdom of Bahrain has witnessed burgeoning municipal solid waste (MSW) generation rate due to socio-economic development. The authorities of this Small Island Developing State, which is located in arid environment, plan to produce 5% of the total electricity demand from renewable energy sources by 2025 and then double it to 10% by 2035. The US Environmental Protection Agency’s Landfill Gas Emission Model software was used to estimate the generation of biogas from MSW at the Askar Landfill site. Results envisaged that maximum landfill gas (LFG) emission rates will be in 2020 following landfill closure by the end of 2019, as an intentional scenario, with a maximum electricity generation potential of 57.4 GWh that could provide power to 488 households. Revenues from carbon credits and electricity sales were US$97.8 million and US$64.8 million, respectively, for the period 2020–2035. The internal combustion engine exhibited the most viable option based on economic analysis of the cost of alternative LFG energy recovery technologies. Our work highlights the potential to use LFG-to-energy technologies to reduce the carbon footprint in arid climates for developing countries with substantial electricity subsidization.

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997–2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO2 emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R2 value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R2 value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.