Mixing of Oil in Water Through Electrical Resistance Tomography and Response Surface Methodology

A Novel Experimental Apparatus for Characterizing Flow Regime in Mechanically Stirred Tanks through Force Sensors

Pressure fluctuations in a mixing tank can provide valuable information about the existing flow regime within the tank, which in turn influences the degree of mixing that can be achieved. In the present work, we propose a prototype for identifying the flow regime in mechanically stirred tanks equipped with four vertical baffles through the characterization of pressure fluctuations. Our innovative proposal is based on force sensors strategically placed in the baffles of the mixing tank. The signals coming from the sensors are transmitted to an electronic module based on an Arduino UNO development board. In the electronic module, the pressure signals are conditioned, amplified and sent via Bluetooth to a computer. In the computer, the signals can be plotted or stored in an Excel file. In addition, the proposed system includes a moving average filtering and a hierarchical bottom-up clustering analysis that can determine the real-time flow regime (i.e., the Reynolds number, Re) in which the tank was operated during the mixing process. Finally, to demonstrate the versatility of the proposed prototype, experiments were conducted to identify the Reynolds number for different flow regimes (static, laminar, transition and turbulent), i.e., 0≤Re≤ 42,955. Obtained results were in agreement with the prevailing consensus on the onset and developed from different flow regimes in mechanically stirred tanks.

Using image-based machine learning and numerical simulation to predict pesticide inline mixing uniformity

BACKGROUND

Accurate pesticide inline mixing uniformity (PIMU) evaluation for direct nozzle injection systems (DNIS) helps evaluate system performance and develop efficient inline mixers. Based on supervised machine learning (ML), inline mixing images and computational fluid dynamics (CFD) simulations are directly associated for realizing intelligent PIMU predictions.

RESULTS

Image sets can be reduced to less than 3% of the data size at the same time as retaining 98% of information using principal component analysis (PCA). The CFD results, as referenced values for ML, were justified by mixture sampling experiments. Enhanced images for the long-mixing tube effectively trained models including generalized linear model (GLM), support vector regression (SVR), BP-neural network (NNW), and classification and regression trees (CART). By testing the re-collected images, the verification accuracy of GLM was less than 95% and it failed to recognize uniformity differences under varying working conditions, whereas NNW, CART and SVR realized it with an accuracy for NNW and CART higher than 97% and for SVR slightly lower than 97%. By testing images of the jet mixer, the prediction accuracy compared with the CFD results of NNW and CART was also higher than 97%, although that for SVR was relatively lower, and insignificant declines in accuracy were observed on comparing the results with mixture sampling experiments.

CONCLUSION

PCA facilitates evaluations of CFD-referenced PIMU using image-based ML. Models trained by enhanced image sets of the long-mixing tube have satisfactory performance. NNW and CART performed slightly better than SVR, and they can be used as tools to improve the rationality when evaluating PIMU in DNIS. © 2022 Society of Chemical Industry.

Review of applications of electrical resistance tomography to chemical engineering

In spite of decades of study and investigation, the research on tomography and electrical resistance tomography (ERT) in particular, remains to be focus of immense scientific significance. ERT provides the ability to measure conductivity distribution inside a process plant and delivers time evolving multidimensional information. Such important and otherwise inaccessible information enhances critical process knowledge whilst improving the design and function of the process equipment. ERT has been employed in a variety of fields including chemical engineering. This paper reviews previous research carried out on the application of ERT within the chemical engineering arena. The applications are classified based on the objective of ERT measurements, the unit operations ERT has been utilized on, the media under examination, and also other technologies and data processing techniques used in combination with ERT. The objective of this taxonomy is to offer the reader with a broad insight into the current situation of ERT related research and developed applications in the chemical engineering field and to assist in the identification of research gaps for future investigation.