On the usage of acoustic properties combined with an artificial neural network – A new approach of determining presence of dairy fouling

A Novel Simulated Moving Plug Flow Crystallizer (SM-PFC) for Addressing the Encrustation Problem: Simulation-Based Studies on Cooling Crystallization

The plug flow crystallizer (PFC) is a promising candidate in the move toward adoption of continuous manufacturing in the pharmaceutical industry. However, a major concern for the smooth running of PFCs is the encrustation or fouling which can result in blockage of the crystallizer or unplanned shutdown of the process. In order to address this problem, simulation studies are carried out to explore the feasibility of a novel simulated-moving PFC (SM-PFC) configuration that can run uninterrupted in the presence of heavy fouling without compromising the desired critical quality attributes of the product crystals. The key concept of the SM-PFC lies in the arrangement of the crystallizer segments where a fouled segment is isolated, while a clean segment is simultaneously brought online avoiding fouling-related issues and maintaining uninterrupted operation. The inlet and outlet ports are also changed appropriately so that the whole operation mimics the movement of the PFC. The simulation results suggest that the proposed PFC configuration could be a potential mitigating approach for the encrustation problem enabling continuous operation of the crystallizer in the presence of heavy fouling while maintaining the product specifications.

A review of ultrasonic sensing and machine learning methods to monitor industrial processes

Supervised machine learning techniques are increasingly being combined with ultrasonic sensor measurements owing to their strong performance. These techniques also offer advantages over calibration procedures of more complex fitting, improved generalisation, reduced development time, ability for continuous retraining, and the correlation of sensor data to important process information. However, their implementation requires expertise to extract and select appropriate features from the sensor measurements as model inputs, select the type of machine learning algorithm to use, and find a suitable set of model hyperparameters. The aim of this article is to facilitate implementation of machine learning techniques in combination with ultrasonic measurements for in-line and on-line monitoring of industrial processes and other similar applications. The article first reviews the use of ultrasonic sensors for monitoring processes, before reviewing the combination of ultrasonic measurements and machine learning. We include literature from other sectors such as structural health monitoring. This review covers feature extraction, feature selection, algorithm choice, hyperparameter selection, data augmentation, domain adaptation, semi-supervised learning and machine learning interpretability. Finally, recommendations for applying machine learning to the reviewed processes are made.

Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.

Characterization of the Evolution of Crystallization Fouling in Membranes

Liquid-to-air membrane energy exchangers (LAMEEs) are promising in heating, ventilating, and air-conditioning applications because they are able to use semipermeable membranes to transfer heat and moisture between air and liquid desiccant streams. However, the development of crystallization fouling in membranes may pose a great risk to the long-term performance of LAMEEs. The main aim of this paper is to characterize the evolution of crystallization fouling in membranes through the use of both noninvasive and invasive methods. Noninvasive methods are used to study the development of fouling in the LAMEE by monitoring the changes in moisture flux through the membrane and overall moisture-transfer resistance of the LAMEE. On the other hand, invasive methods are implemented to characterize fouled membranes by using optical microscopy and scanning electron microscopy (SEM) to depict the morphology of crystal deposits and energy-dispersive X-ray spectroscopy (EDX) to identify the composition of the deposits. Experiments are performed by using air to dehydrate MgCl₂(aq) at two operating conditions of low and high fouling rates. The results show that the moisture flux decreases and the moisture-transfer resistance increases more considerably during the test at the high fouling rate than in the test at the low fouling rate. SEM micrographs show that cake crystal deposits cover the membrane surface in the test at the high fouling rate, whereas only few crystal particles are observed on the membrane in the test at the low fouling rate. Furthermore, the crystal deposits undergo more structural changes in the tests at the high fouling rate than in the tests at the low fouling rate, possibly because of the higher moisture transfer rate through the membrane in the tests at the high fouling rate. Finally, the SEM-EDX analysis confirms that the crystal deposits primarily consist of Mg, Cl, and O elements.

Erratum: Acoustic Sensing and Signal Processing Techniques for Monitoring Milk Fouling Cleaning Operations

[This corrects the article DOI: 10.1002/elsc.201400235.].

Ultrasonic sensor for predicting sugar concentration using multivariate calibration

This paper presents a multivariate regression method for the prediction of maltose concentration in aqueous solutions. For this purpose, time and frequency domain of ultrasonic signals are analyzed. It is shown, that the prediction of concentration at different temperatures is possible by using several multivariate regression models for individual temperature points. Combining these models by a linear approximation of each coefficient over temperature results in a unified solution, which takes temperature effects into account. The benefit of the proposed method is the low processing time required for analyzing online signals as well as the non-invasive sensor setup which can be used in pipelines. Also the ultrasonic signal sections used in the presented investigation were extracted out of buffer reflections which remain primarily unaffected by bubble and particle interferences. Model calibration was performed in order to investigate the feasibility of online monitoring in fermentation processes. The temperature range investigated was from 10 °C to 21 °C. This range fits to fermentation processes used in the brewing industry. This paper describes the processing of ultrasonic signals for regression, the model evaluation as well as the input variable selection. The statistical approach used for creating the final prediction solution was partial least squares (PLS) regression validated by cross validation. The overall minimum root mean squared error achieved was 0.64 g/100 g.

Investigating and understanding fouling in a planar setup using ultrasonic methods

Fouling is an unwanted deposit on heat transfer surfaces and occurs regularly in foodstuff heat exchangers. Fouling causes high costs because cleaning of heat exchangers has to be carried out and cleaning success cannot easily be monitored. Thus, used cleaning cycles in foodstuff industry are usually too long leading to high costs. In this paper, a setup is described with which it is possible, first, to produce dairy protein fouling similar to the one found in industrial heat exchangers and, second, to detect the presence and absence of such fouling using an ultrasonic based measuring method. The developed setup resembles a planar heat exchanger in which fouling can be made and cleaned reproducible. Fouling presence, absence, and cleaning progress can be monitored by using an ultrasonic detection unit. The setup is described theoretically based on electrical and mechanical lumped circuits to derive the wave equation and the transfer function to perform a sensitivity analysis. Sensitivity analysis was done to determine influencing quantities and showed that fouling is measurable. Also, first experimental results are compared with results from sensitivity analysis.

Suspension flow in microfluidic devices--a review of experimental techniques focussing on concentration and velocity gradients

Microfluidic devices are an emerging technology for processing suspensions in e.g. medical applications, pharmaceutics and food. Compared to larger scales, particles will be more influenced by migration in microfluidic devices, and this may even be used to facilitate segregation and separation. In order to get most out of these completely new technologies, methods to experimentally measure (or compute) particle migration are needed to gain sufficient insights for rational design. However, the currently available methods only allow limited access to particle behaviour. In this review we compare experimental methods to investigate migration phenomena that can occur in microfluidic systems when operated with natural suspensions, having typical particle diameters of 0.1 to 10 μm. The methods are used to monitor concentration and velocity profiles of bidisperse and polydisperse suspensions, which are notoriously difficult to measure due to the small dimensions of channels and particles. Various methods have been proposed in literature: tomography, ultrasound, and optical analysis, and here we review and evaluate them on general dimensionless numbers related to process conditions and channel dimensions. Besides, eleven practical criteria chosen such that they can also be used for various applications, are used to evaluate the performance of the methods. We found that NMR and CSLM, although expensive, are the most promising techniques to investigate flowing suspensions in microfluidic devices, where one may be preferred over the other depending on the size, concentration and nature of the suspension, the dimensions of the channel, and the information that has to be obtained. The paper concludes with an outlook on future developments of measurement techniques.