Ankur Sarkar

Nanotechnology-based membrane-separation process for drinking water purification

The conventional membrane-separation process for water purification consumes a considerable amount of energy and has some limitations therefore nanotechnology-based membrane-separation process has become an alternative as it consumes less energy and eliminates limitations of conventional process. Mainly, nanograde titanium dioxide, silica, sliver, zeolite, carbon nanotube (CNT), aquaporin, vertically aligned CNTs, block copolymers, and so on. have been incorporated to fabricate nanocomposite membrane, which is very much effective for degradation of organic compounds, bacteria, spores, and virus. Thus it has potential application in water disinfection and removal of organic contaminants from water. Moreover, antifouling properties, chemical, mechanical, and thermal stability of such membranes are enhanced. Therefore, modified membranes have shown higher flux, permeability, and selectivity than conventional membrane. Commercialization of some of the nanomaterial-based membranes has already been started in the field of water purification and rest of them are in lab-scale or pilot-scale development stage.

Two indigenous high sheared membrane modules' performance expatiation for the ultrafiltration of polyethylene glycol

Performance of the ultrafiltration (UF) with anti-thixotropic polyethylene glycol (PEG) 6000 was investigated in the present study using two forward-looking high sheared membrane modules called radial flow membrane module (RFMM) and turbine flow membrane module (TFMM). In addition, the present study has been extended to investigate the effect of both polyether sulfone (PES) and poly sulfone (PSf) membranes equipped within the individual module during ultrafiltration of hydrophilic PEG 6000. Moreover, applicability of the membrane-module combination was judged along with the membrane reusability and the power consumed by these modules. TFMM equipped with PSf membrane was found to be more effective in this respect at 20 kg m−3 initial PEG 6000 concentration and 0.2 MPa in creating maximum shear rate over the membrane attributing to moderate flux with low pump energy consumption. Around 97% water flux regains after two consecutive experiments with maximum initial PEG 6000 concentration in order to conclude the reusability of the post-washed membrane even the membrane was exposed to the highest level of concentration.

Modelling aspects of carbon dioxide capture technologies using porous contactors: a review

Carbon dioxide (CO2) capture is now becoming a promising area of research in current days due to its contribution towards global warming, and the demand for it is continuously growing among the research fraternities. In any chemical process, one of the main challenges that control the performance of the process requires a complete evaluation of the parameters, and this can only be achieved through development of a proper mathematical model. This is also true for the upcoming field-CO2 capture process to ensure efficient CO2 removal from the gas stream with proper parametric control of the process. Thus, the current study is an in-depth review on the mathematical formulation done so far on the CO2 capture process, their merits and demerits.

Fuzzy evaluated quantum cellular automata approach for watershed image analysis

Fuzzy approaches in a low-level image processing method to partition the homogeneous regions are important challenges in image segmentation. The analysis of the fuzziness in data produces comparable or improved solutions compared with the respective crisp approaches. The novel approach proposed in this chapter has been found to enhance the functionality of the fuzzy rule base and thus enhance the established potentiality of new fuzzy-based segmentation domain with the help of partitioned quantum cellular automata. Image segmentation among overlapping land cover areas on satellite images is a very crucial problem. To detect the belongingness is an important problem for mixed-pixel classification. This new approach to pixel classification is a hybrid method of fuzzy c-means and partitioned quantum cellular automata methods. This new unsupervised method is able to detect clusters using a two-dimensional partitioned cellular automaton model based on fuzzy segmentations. This method detects the overlapping areas in satellite images by analyzing uncertainties from fuzzy set membership parameters. As a discrete, dynamical system, a cellular automaton explores uniformly interconnected cells with states. In the second phase of our method, we use a two-dimensional partitioned quantum cellular automaton to prioritize allocations of mixed pixels among overlapping land cover areas. We tested our method on the Tilaiya Reservoir catchment area of the Barakar River for the first time. The clustered regions are compared with well-known fuzzy C-means and K-means methods and also with the ground truth information. The results show the superiority of our new method.