All

The temperature, feed rate, length of mass transfer zone, utilization factor and partial pressure are the parameters considered for fixed bed sorption of CO2 from N2/CO2 mixture. The breakthrough time relies strongly on the temperature and feed rate. The prolonged breakthrough and saturation times have been realized for AC. The response curves of AC are vastly steep signifying the maximal utilization of bed capacity at the breakpoint. In general, the length of MTZ increases with raised temperature and feed flow rate. The capacity utilization factor reduces with raised temperature and feed flow rate. A utilization factor of 0.919 was determined for AC. The maximal capacity for CO2 reduces significantly with an increased temperature. The maximal capacities of 32.99 gm CO2/Kg was determined at a temperature of 298 K for AC. The capacity improves considerably with CO2 partial pressure and AC exhibited higher adsorption capacity compared to SG. The capacity improves considerably with increased feed rates and maximal capacity of 39.14 g CO2/Kg adsorbent was determined for AC at the feed rate of 8.33 x10-3 m3/sec. Owing to higher sorption capacity and utilization factor, the AC may be used for economical separation of CO2 from N2/CO2 mixture

Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.

This study was conducted to investigate the effect of different types of organic wastes (grass and newspaper wastes, cow dung and their combination) on physicochemical properties during vermicomposting using Eisenia fetida. Three set-ups were conducted in which three experiments were for composting (controls) indicated as C1 for newspaper waste, C2 for grass waste and C3 for cattle dung and the corresponding replicates for vermicomposting processes for grass and cattle dung were T1 to T7 and mixed of newspaper, grass and cattle dung were T8-T13. A significant difference was detected among weight and number of Eisenia fetida in treatments, which the highest weight and population growth of worms occurred in T7 (60% of newspaper, 30% of cattle dung and 10% of grass waste). Compared to control treatment (without earthworms), vermicomposting treatment resulted in a decrease of organic carbon and C/N ratio after 30 days. Vermicomposting caused significant reduction in organic carbon (3.9-32.8% ) and C/N ratio (7.9–33.6%). The heavy metals degradation was evaluated after 90 days and at the end of the experiment the concentration of As, and Cd significantly decreased. This study clearly indicates that vermicomposting is a suitable technology for bioconversion of newspaper waste, grass clipping and cow dung to valuable material.

In this research, capability of adsorption of modified chitosan Nano composites with functionalized mesoporous silica which synthesized by electrospinning method in the removal of direct black ANBN dye has been studied. Silicate mesoporous were functionalized during synthesis process and then the intended Nano fibers were produced by using of electrospinning device. Synthesized Nano fibers were recognized by analyzing the FT-IR, XRD, FE-SEM, and TEM and tension test. The removal of direct black ANBN dye from aqueous solution in a continuous system was done by this adsorbent by studying the effects of some parameters such as type and amount of adsorbent, pH, initial concentration of colored solution and contact time; And at the end finding appropriate isotherms for direct black dye adsorption was done. The result show that simple CTS/SBA-15 have the best removal efficiency in 40 min of contact time, in acidic pH, with 0.05 gr adsorbent and 60 mg/L initial concentration. By examining the adsorption isotherms, it has been clearly defined [that] Removal of this dye follows Freundlich model. In order to compare accurately and evaluate the efficiency of the synthesized sample, the adsorption of Direct Black ANBN was investigated by two different adsorbents: CTS/SBA-15 and Chitosan. According to the results, CTS/SBA-15 Nano fibers have suitable efficiency for removal of direct black ANBN dye.