Experimental studies in antisolvent crystallization: Effect of antisolvent ratio and mixing patterns

18-25The crystals size and distribution play an important role in drug properties which has a major impact on the performance e.g., stability, solubility and bioavailability. The crystal size distribution (CSD) depends on the hydrodynamics and local degree of supersaturation in the crystallizer. In this study, we have investigated the effects of various operating conditions (antisolvent ratio, power, agitator design) using different mixing techniques such as impellers and ultrasound on CSD and average crystal size (ACS). It is found that mixing plays a dominant role in CSD and ACS. The hydrofoil (axial flow impeller) provides a wide range of ACS (406 to 240 μm) at lower power as compared to Rushton turbine (radial flow impeller) (395 to 375 μm). The mixed flow impeller produces the intermediate crystal size (365 to 345 μm). The increase in the antisolvent ratio results in a decrease in ACS. The same results observed for the power input

Application of artificial intelligence to predict flow assisted corrosion in nuclear/thermal power plant

Flow assisted corrosion (FAC) is a wall-thinning phenomena of carbon steel pipe in nuclear and thermal power plant. Due to FAC, many accidents have taken place in nuclear plants resulting in casualties. In FAC, dissolution of iron from the iron-oxide fluid interface at pipe wall takes place and it is affected by pH, oxygen concentration, flow rate, temperature and chromium content of piping material. Due to complex interaction of these parameters, FAC prediction is difficult using conventional modeling tools and experimental evaluation is time consuming and costly. In this work, artificial neural network (ANN) has been used for FAC prediction using 320 data points collected from published literature. The neural network training was carried out using Lavender-Marquardt back-propagation algorithm in Matlab. The results show that ANN is a powerful tool for predicting FAC rate with regression coefficient above 90% and hence it can be very useful by regular training of the model with actual operational data in safety management and long term planning in nuclear/thermal power plant. A sensitivity analysis with respect to each parameter has been carried out using ANN model. It is observed that FAC rate is lower under alkaline conditions and goes through a maxima in a temperature range of 140 to 150°C

Computational and experimental fluid dynamics of jet loop reactor

A computational analysis using standard k-ε model, RSM and LES has been carried out for jet loop reactors (JLR) to investigate the mean and turbulence quantities. These simulations have revealed that the flow in JLR was different from the self-similar round jets. RSM and LES showed better agreement with PIV measurements compared with standard k-ε model. The modeled turbulence production and transport in k-ε model overpredicted those estimated from LES data. To reduce the limitations, modified k-ε models have been evaluated for JLR. Also, a hybrid k-ε model has been suggested, which was found to perform better than other modified k-ε models. This model was also found to hold for stirred tank reactors (STRs). Mixing time analysis has been carried out for JLR and STR at same power consumption. It has been shown that JLR can be inferior to STR if proper nozzle diameter is not selected

Scheduling of Energy-Integrated Batch Process Systems Using a Pattern-Based Framework

In this paper, a novel pattern-based method is developed for the generation of optimal schedules for energy-integrated batch process systems. The proposed methodology is based on the analysis of available schedules for the identification of repetitive patterns. It is shown that optimal schedules of energy-integrated batch processes are composed of several repeating sections (or building blocks), and their sizes and relative positions are dependent on the scheduling horizon and constraints. Based on such a decomposition, the proposed pattern-based algorithm generates an optimal schedule by computing the number and sequence of these blocks. The framework is then integrated with rigorous optimization-based approach wherein it is shown that the learning from the pattern-based solution significantly improves the performance of rigorous optimization. The main advantage of the pattern-based method is the significant reduction in computational time required to solve large scheduling problems, thus enabling the possibility of on-line rescheduling. Three literature examples were considered to demonstrate the presence of repeating patterns in optimal schedules of energy-integrated batch systems. The effectiveness of the proposed methodology was illustrated using an integrated reactor-separator system

Dynamics of flow structures and transport phenomena, 2. Relationship with design objectives and design optimization

There have been several approaches in the literature to identify and characterize flow structures qualitatively as well as quantitatively. In the first part of this review, the methodologies and applications of various experimental fluid dynamics and computational fluid dynamics techniques, as well as mathematical techniques, have been discussed. Their chronological developments, and relative merits and demerits, have been presented to allow readers to make a judgment as to which techniques to adopt. In the present part of the review series, a stepwise procedure is suggested for the design of equipment using flow structure knowledge. An attempt has been made to relate the structure properties (such as age, penetration depth, size, shape, and energy content distribution) to the design parameters (such as mixing time, heat- and mass-transfer coefficient, drag coefficient, dissipation rate, etc.). This understanding of flow structures has brought improvements in the formulations of heuristic models of mass and heat transfer. This review makes an effort in developing insights into the views of earlier established analytic and heuristic theories of heat and mass transfer. The recently revealed dynamics of flow structures (as uncovered through the use of various techniques) has helped in furthering the efforts of developing new theories of heat, mass, and momentum transfer. Such an understanding between the structure dynamics and the transport phenomena has helped in the optimization of flow pattern (for instance, maximization of ratios of heat and mass transfer, as well as mixing, with respect to energy input). In this direction, some success stories have been described that have already been implemented in industry and have good potential for implementation

Experimental studies in antisolvent crystallization: Effect of antisolvent ratio and mixing patterns

The crystals size and distribution play an important role in drug properties which has a major impact on the performance e.g., stability, solubility and bioavailability. The crystal size distribution (CSD) depends on the hydrodynamics and local degree of supersaturation in the crystallizer. In this study, we have investigated the effects of various operating conditions (antisolvent ratio, power, agitator design) using different mixing techniques such as impellers and ultrasound on CSD and average crystal size (ACS). It is found that mixing plays a dominant role in CSD and ACS. The hydrofoil (axial flow impeller) provides a wide range of ACS (406 to 240 μm) at lower power as compared to Rushton turbine (radial flow impeller) (395 to 375 μm). The mixed flow impeller produces the intermediate crystal size (365 to 345 μm). The increase in the antisolvent ratio results in a decrease in ACS. The same results observed for the power input

Dynamics of flow structures and transport phenomena, 1. Experimental and numerical techniques for identification and energy content of flow structures

Most chemical engineering equipment is operated in the turbulent regime. The flow patterns in this equipment are complex and are characterized by flow structures of wide range of length and time scales. The accurate quantification of these flow structures is very difficult and, hence, the present design practices are still empirical. Abundant literature is available on understanding of these flow structures, but in very few cases efforts have been made to improve the design procedures with this knowledge. There have been several approaches in the literature to identify and characterize the flow structures qualitatively as well as quantitatively. In the last few decades, several numerical as well as experimental methods have been developed that are complementary to each other with the onset of better computational and experimental facilities. In the present work, the methodologies and applications of various experimental fluid dynamics (EFD) techniques (namely, point measurement techniques such as hot film anemometry, laser Doppler velocimetry, and planar measurement techniques such as particle image velocimetry (PIV), high speed photography, Schlieren shadowgraphy, and the recent volume measurement techniques such as holographic PIV, stereo PIV, etc.), and the computational fluid dynamics (CFD) techniques (such as direct numerical simulation (DNS) and large eddy simulation (LES)) have been discussed. Their chronological developments, relative merits, and demerits have been presented to enable readers to make a judgment as to which experimental/numerical technique to adopt. Also, several notable mathematical quantifiers are reviewed (such as quadrant technique, variable integral time average technique, spectral analysis, proper orthogonal decomposition, discrete and continuous wavelet transform, eddy isolation methodology, hybrid POD-Wavelet technique, etc.). All three of these tools (computational, experimental, and mathematical) have evolved over the past 6-7 decades and have shed light on the physics behind the formation and dynamics of various flow structures. The work ends with addressing the present issues, the existing knowledge gaps, and the path forward in this field

Effect of flow structures on heat transfer in single and multiphase jet reactors

High frequency experimental measurements by hot film anemometry (HFA) of liquid velocities and temperature in the region of vapor-liquid (VL) and solid-liquid (SL) interfaces for two important reactor types, namely, condensation jet and jet loop reactors, have been studied for their heat transfer characteristics. An algorithm for flow structure identification has been devised from velocity data based on (i) zero crossings and (ii) continuous wavelet transform. The wavelet transform algorithm is especially found to be useful in accurately estimating both the age and size distributions of eddies near interfaces in a multiscale framework. Using these distributions, it is shown that the calculated values of heat transfer coefficients (HTC) at the SL and VL interfaces show remarkable correspondence with the HTC values obtained experimentally from instantaneous temperature measurements. For this purpose, a modified capacitance model has been proposed that takes into account the information about both the age and size distributions. The results obtained by the present methodology show the improvement possible for calculating the HTC at interfaces when compared with the earlier surface renewal models. It may therefore be used to study the interaction between flow dynamics and heat transfer behavior in chemical process equipment