Adsorption technology due to its potentially low energy consumption, simple operation and flexibility in design to meet different demands is fast becoming popular and is now widely considered in the area of CO2 capture. Adsorbents play a vital role in any adsorption technology. Therefore, the behavior of adsorbents under different conditions during an adsorption process needs to be investigated. In this study, the behavior of polyaspartamide as an adsorbent during post-combustion CO2 capture was investigated using kinetic and non-kinetic models. Bohart-Adams and Thomas models were the non-kinetic models explored to ascertain whether external mass transfer dominated the overall system kinetics during the CO2 adsorption onto polyaspartamide. The kinetics of adsorption of polyaspartamide was studied using Lagergen's pseudo 1st order, Lagergen's pseudo 2nd order and the Avrami kinetic models in order to understand whether the adsorption process was a physical, chemical or physiochemical process. The experimental validation of the model prediction was carried out in a laboratory-sized packed bed adsorption column at an operating pressure of 2 bar; gas flow rate of 1.5-2.5 ml/s, and a temperature range of 303-333 K using 0.1 g of the adsorbent. The experimental breakthrough curve showed a superior fit with the Bohart-Adams model. For the kinetic study, Avrami kinetic model displayed a better fit with kinetic data at all temperatures studied. The non-kinetic model revealed that external mass transfer governed the adsorption of CO2 onto polyaspartamide while the kinetic study revealed that the mechanism of adsorption of CO2 onto polyaspartamide was more of physical than chemical (physiochemical).
Process modelling and techno-economic analysis of NG-DRI/EAF and H2 DRI/EAF steel production providing insights into process design and break-even hydrogen prices.
The search for viable alternatives to conventional materials in biomedical applications is as important as the movement for the adoption of a sustainability approach in the production of polymer nanocomposites for prosthetic purposes. Carbon nanotube (CNT) reinforced polymer nanocomposites have become the center of the present prosthetic industry due to their unparalleled strength-to-weight characteristics. However, the categories of polymers used for this purpose and their long-term impact on the environment have generated controversies among researchers. The adequacy, affordability, and sustainability of materials for the development of prosthetics are some of the common concerns. Consequently, this review addresses concerns about the adherence to SDGs in biomedical manufacturing which focuses on material selection considering environmental impacts. In addition, contributions from previous research were reviewed based on the remarkable increase in the number of publications on CNT-reinforced polymer nanocomposites over the last 10 years. Various findings by researchers in the field who used natural rubber and other polymers as host matrices were analyzed from the perspective of sustainability. While considerable progress has been made in the use of other polymers in the biomedical field, only a few publications have targeted natural rubber. This review provides insights into opportunities for sustainable production and consumption of devices with biodegradable CNT/natural rubber nanocomposites.
A systematic study of the diffusion mechanism and effect of mass transfer limitation during the adsorption of CO2 onto polyaspartamide is presented using a differential adsorption bed method, carried out in a 100 × 60 × 40 mm packed-bed adsorption unit. The rate-limiting step where mass transfer limitation is dominant was studied using diffusion models. It was observed that intraparticle diffusion mechanism is the major contributor to the resistance offered to the transport of gas molecule through polyaspartamide. The behaviour of polyaspartamide, based on the intraparticle diffusion rate parameter derived from the plots of CO2 adsorbed versus the square root of time, signified that the adsorption mechanism involved both film and intraparticle diffusion. The intraparticle diffusion parameter (kid) obtained was dependent on temperature as well as intraparticle convection effects and ranged from 1.24 × 10−4 to 2.13 × 10−4 ms−1. The Biot number (Bi) values were all greater than 10 (ranged from 17.80 – 30.74), confirming that the intraparticle diffusion was the rate-limiting step and heat transfer is more by conduction from the gas film layer than convection within the pores of polyaspartamide. Results from this study provide an important basis for future scale-up and optimisation of CO2 capture process using polyaspartamide.
Parametric effect of moisture and influence of operating variables on the adsorption behaviour of polyaspartamide during CO2 capture was investigated in this study using experimental and modelling approach. Individual effects of operating conditions (e.g. pressure, temperature and gas flow rates) as well as the effect of moisture on the adsorption capacity of polyaspartamide were methodically investigated using Dubinin–Raduskevich model. Results from the investigations reveal that the presence of moisture in the flue gas had an incremental effect on the adsorption capacity of polyaspartamide; thereby showcasing the potential of polyaspartamide as a suitable hydrophilic material for CO2 capture in power plants. In addition, pressure, temperature and gas flow rates at 200 kPa, 403 K, and 1.5 mL/s, respectively, significantly influenced the CO2 adsorption capacity of polyaspartamide. Physisorption and chemisorption both governed the adsorption process while equilibrium studies at different temperatures showed that Langmuir isotherm could adequately describe the adsorption behaviour of the material with best fit with R2 > 0.95.
High energy consumption is a major challenge currently threatening many industrial processes. This has made industrial processes such as absorptive CO2 capture expensive and energy intensive. However, research has shown that the application of Heat Exchanger Networks has the potential of minimizing energy demands in many industrial processes due to its energy recovery advantage. In this study, a sequential procedure is presented for the synthesis of heat exchanger network for multi-period operations with specified uncertainties in flow rates and variations in inlet and outlet temperatures of process streams. The synthesis task in this study was sequentially decomposed into three stages. Temperature interval method was used to determine the loads and minimum utilities required by the network in the first stage. Determination of the minimum number of units was considered in the second stage while the third stage was dedicated to the derivation of a network configuration and sizing of heat exchangers to determine the capital cost using area targeting technique. Efficacy of the proposed methodology was tested using an example from literature. A new heuristic rule was established and the network topologies obtained using the proposed approach testifies to the applicability of HENs for energy minimization during absorptive CO2 capture.
Abstract The local sourcing of feedstock for energy generation will reduce costs in the power plant, and promote energy sustainability. Most times, potential investors in this area show interest about understanding the profitability of the business because, the information boosts the confidence of the investors in the project, and gives them the opportunity of making a short and long term plans about the business. The emissions arising from the energy plant is an important aspect of the venture that requires proper attention, otherwise the costs of emission control may consume a greater part of the profit, hence rendering the business un-viable. Nigeria and South Africa (SA) have abundant biomass (e.g. corn cob, sugarcane bagasse, & pine saw dust) coal and tyre that can be used as fuel in an energy plant. A 10 MW CHP plant was fired with coal and biomass, and tyre obtained from Nigeria and South Africa (SA) respectively, at ratios of 1:1, 3:2, and 4:1 to study the emissions and profits in the plant. An empirical model was employed to estimate the annual amount of feedstock and feed rate required for the plant, after which, an artificial neural network (ANN); Levenberg-Marquardt algorithm was used to predict the emissions and profits in the plant for 20-year-investment period with feedstock costing (WFC) and without feedstock costing (WOFC). The profit obtained from the South African feedstock, WFC and WOFC; produced about 45.18 % and 36.83 % ($3, 900, 000.07 and $3, 179, 184.49) higher profits than the Nigerian feedstock, but the CO, NOX, & SO2 emissions from Nigerian feedstock were lower than that of SA. The findings from this study could be used as a platform for decision making by potential investors and stake-holders, and further research and development in the area.
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