Ajay Kumar Ray

In the current study, a water treatment approach integrating freezing technology, so-called cryopurification, and microbial fuel cell (MFC) process is proposed and tested towards zinc removal. Contaminated water samples used for laboratory experiments were received from the Faro Mine site, Yukon, Canada. Through cryopurification, the effect of freezing temperature, mixing and the direction of ice front propagation on zinc removal from the Faro mine water was investigated and quantitively analyzed. The MFC was used to treat a post-cryopurification brine, both at a laboratory scale. When the coolant temperature ranged from − 5 to − 1 ̊C and 180 rpm solution mixing was used, up to 80–95% of zinc was removed after a single freezing cycle. The results of laboratory experiments demonstrated that zinc concentrations in mine water can be reduced by cryopurification to 0.5 mg/L (effluent quality standard) under optimal experimental conditions. The MFC process was run for 120 h to test the capacity of the microorganism (Shewanella oneidensis) towards zinc removal from the brine concentrated by freezing. Based on the results of laboratory experiments, MFC showed a reliable and high zinc removal up to 90–93% with Shewanella oneidensis incubated in the anode. The MFC generated a power density and open-circuit voltage with a maximum result of 8.8 mW/m² and 168.5 mV, respectively.

Article Preface: special issue dedicated to the “International Conference on Energy Sustainability and Advanced Materials – 2022 (ICESAM-2022)” part of the Energy Summit-2022 UPES, Dehradun, India was published on December 20, 2023 in the journal International Journal of Chemical Reactor Engineering (volume 0, issue 0).

The determination of physicochemical properties of crude oils is a very important and time‐intensive process that needs elaborate laboratory procedures. Over the last few decades, several correlations have been developed to estimate these properties, but they have been very limited in their scope and range. In recent years, methods based on spectral data analysis have been shown to be very promising in characterizing petroleum crude. In this work, the physicochemical properties of crude oils using Fourier transform infrared (FTIR) spectrums are predicted. A total of 107 samples of FTIR spectral data consisting of 6840 wavenumbers is used. One dimensional convolutional neural networks (CNNs) were used employing FTIR spectral data as the one‐dimensional input and Keras and TensorFlow were used for model building. The Root Mean Square Error decreased from 160 to around 60 for viscosity when compared to previous machine learning methods like partial least squares (PLS), principal component regression (PCR), and partial least squares regression with genetic algorithm (PLS‐GA) on the same data. The important hyper‐parameters of the CNN were optimized. In addition, a comparison of results obtained with different neural network architectures is presented. Some common preprocessing techniques were also tested on the spectral data to determine their impact on model performance. To increase interpretability, the intermediate neural network layers were analyzed to reveal what the convolutions represented, and sensitivity analysis was done to gather key insights about the wavenumbers that were the most important for prediction of the crude oil properties using the neural network.

Photocatalysis application in water treatment has been the object of many researchers worldwide in recent decades. However, there are limited commercial applications due to low photon transfer efficiency, which create barriers leading to challenges in making the process efficient and economically feasible. Fixed UV/visible light sources, which are generally located outside the reactor or encapsulated in quartz tube inside the reactor are the source of energy to activate photocatalyst generating powerful oxidants such as electrons and holes. Suspended waterproof LED visible lights were employed to enhance photon transfer efficiency. Consequently, the required energy was lower resulting in negligible temperature increase and eliminated the need for an external cooler, no need for quartz (UV transparent) or treated glass reactors, enhanced mixing due to continuous movement of light bulbs by convective currents, and minimum/no attenuation. Direct Blue 15 (DB15) dye was used as model compound and the photocatalyst was P25 TiO₂ (Average particle: 30 nm, Surface area: 50 m² g⁻¹). The samples taken at different time intervals were analyzed by UV-Vis. spectrophotometer (UV-3600), and TOC-V CPN total organic carbon analyzer (both from Shimadzu). It was found that for the same level of degradation, the degradation rate increased by about 50% compared to conventional fixed light photoreactor. Overall, the cost of the operation can be reduced substantially, paving the road for feasible commercialization of the process.

Simulated moving bed (SMB) chromatography is a highly efficient adsorption-based separation technology with various industrial applications. At present, its application has been successfully extended to the biochemical and pharmaceutical industrial sectors. SMB possesses the advantages of high product purity and yield, large feed treatment capacity, and simple process control due to the continuous operation mode and the efficient separation mechanism, particularly for difficult separation. Moreover, SMB performs well, particularly for multi-component separation or complicated systems’ purification processes in which each component exhibits similar properties and low resolution. With the development of the economy and technology, SMB technology needs to be improved and optimized to enhance its performance and deal with more complex separation tasks. This paper summarizes the typical variants or modifications of the SMB process through three aspects: zone variant, gradient variant, and feed or operation variant. The corresponding modification principles, operating modes, advantages, limitations, and practical application areas of each variant were comprehensively investigated. Finally, the application prospect and development direction were summarized, which could provide valuable recommendations and guidance for future research in the SMB area.

The agricultural industry uses substantial amounts of water (the highest in the world) mostly for irrigation purposes. Rapid population growth and, consequently, growing demand for food have increased the use of pesticide to have higher yield for crops and other agricultural products. Wastewater generated as a result of excessive use of pesticides/herbicides in agricultural industry is becoming a global issue specifically in developing countries. Over 4,000,000 tons of pesticides are currently used in the world annually and high concentrations above their threshold limits have been detected in water bodies worldwide. The generated wastewater (contaminated with pesticides) has negative impacts on human health, the ecosystem, and the aquatic environment. Recently, biodegradable and biocompatible (including plant-based) pesticides have been introduced as green and safe products to reduce/eliminate the negative impacts of synthetic pesticides. Despite positive advantages of biopesticides, their use is limited due to cost and slow interaction with pests compared to chemical pesticides. Pesticides may also react with water and constituents of soil resulting in formation of intermediates having different physical and chemical properties. Diffusion, dispersion, and permeation are main mechanisms for transfer of pesticides in soil and water. Pesticides may degrade naturally in nature; however, the time requirement can be very long. Many mathematical models have been developed to simulate and estimate the final fate of pesticides in water resources. Development of new technologies and environmentally friendly pesticides to reduce water contamination is becoming increasingly important.