Carbon dioxide in an indoor environment is one of the air pollutants monitored by the Indoor Air Quality Ordinance of Taiwan. It is necessary for people and the air-conditioning industry to take appropriate measures to minimize the emission and prevent creating an unacceptable indoor air quality (IAQ). This study presents the performance of sorption-type air filters to help people and the air-conditioning industry to understand and apply the best practical control to prevent CO₂ emissions from causing IAQ problems. The adsorption of CO₂ with a sorption-type filter with activated carbon before and after impregnating treatment has been investigated. It has been found that impregnation treatment with MgO and CaO can promote the adsorption of CO₂ on a sorption-type filter, with MgO being the better of the two.

During the production process of commercial carbon fiber reinforced polymers (CFRPs), a silane coupling agent is added to the carbon fiber at the sizing step as a binder to enhance the product’s physical properties. While improving strength, the silane coupling agent results in a silane residue on recovered carbon fibers (rCF) after recycling, which is a disadvantage when using recovered carbon fibers in the manufacture of new materials. In this study, the rCF is recovered from waste carbon fiber reinforced polymers (CFRPs) from the bicycle industry by a microwave pyrolysis method, applying a short reaction time and in an air atmosphere. Moreover, the rCF are investigated for their surface morphologies and the elements present on the surface. The silicon element content changes with pyrolysis temperature were 0.4, 0.9, and 0.2%, respectively, at 450, 550, and 650 °C. Additionally, at 950 °C, silicon content can be reduced to 0.1 ± 0.05%. The uniformity of microwave pyrolysis recycle treatment was compared with traditional furnace techniques used for bulk waste treatment by applying the same temperature regime. This work provides evidence that microwave pyrolysis can be used as an alternative method for the production of rCFs for reuse applications.

This study investigated the efficacy of a novel correlation of power input, energy dissipation rate and mixing time as a potential route to identify the orbitally shaken bioreactor (OSB) system. The Buckingham’s π-theorem was used to designate and transform dimensionless Newton numbers with five relevant power input variables. These variables were empirically varied to evaluate the correlation among the dimensionless numbers. The Newton number decreases with the increased shaking frequency and filling volume. Previous work has focused on optimizing the mixing process by evaluating different shaking and agitation mixing methods. We establish a new mixing process and assessable measurement of the mixing time in the OSB. An innovative explanation of mixing time for the thermal method is proposed. The optimal mixing time is independent of the temperature of filled liquid. The dimensionless mixing number remained constant in the turbulent regime and increasing with the increased liquid viscosity and filling volume. Our findings revealed that the observed correlation is a practical tool to figure the power consumption and mixing efficiency as cell cultivation in all OSB scales and is fully validated when scaling–up system.

We studied different face velocity and carbon dioxide (CO₂) initial concentration to examine the adsorption behavior of calcium oxide (CaO) impregnated activated carbon (AC) filter and also to discuss pseudo-first-order, pseudo-second-order and intra-particle diffusion three kinetic models. The experimental results show that saturation time and saturation capacity were decreased and increased with higher inlet concentration at the same face velocity, respectively. Simulation results show that pseudo-second-order correlation coefficient (r₂² = 0.921) is higher than pseudo-first-order (r₁² = 0.7815) and intra-particle diffusion (r_i² = 0.905). Therefore, the adsorption process of CO₂ onto CaO impregnated AC filter media is appropriate for the pseudo-second-order kinetic model.