Soft sensor development for real-time process monitoring of multidimensional fractionation in tubular centrifuges

High centrifugal acceleration and throughput rates of tubular centrifuges enable the solid–liquid size separation and fractionation of nanoparticles on a bench scale. Nowadays, advantageous product properties are defined by precise specifications regarding particle size and material composition. Hence, there is a demand for innovative and efficient downstream processing of complex particle suspensions. With this type of centrifuge working in a semi-continuous mode, an online observation of the separation quality is needed for optimization purposes. To analyze the composition of fines downstream of the centrifuge, a UV/vis soft sensor is developed to monitor the sorting of polymer and metal oxide nanoparticles by their size and density. By spectroscopic multi-component analysis, a measured UV/vis signal is translated into a model based prediction of the relative solids volume fraction of the fines. High signal stability and an adaptive but mandatory calibration routine enable the presented setup to accurately predict the product’s composition at variable operating conditions. It is outlined how this software-based UV/vis sensor can be utilized effectively for challenging real-time process analytics in multi-component suspension processing. The setup provides insight into the underlying process dynamics and assists in optimizing the outcome of separation tasks on the nanoscale

Sensor based real-time process monitoring for ultra-precision manufacturing processes with non-linearity and non-stationarity

This research investigates methodologies for real-time process monitoring in ultra-precision manufacturing processes, specifically, chemical mechanical planarization (CMP) and ultra-precision machining (UPM), are investigated in this dissertation.The three main components of this research are as follows: (1) developing a predictive modeling approaches for early detection of process anomalies/change points, (2) devising approaches that can capture the non-Gaussian and non-stationary characteristics of CMP and UPM processes, and (3) integrating multiple sensor data to make more reliable process related decisions in real-time.In the first part, we establish a quantitative relationship between CMP process performance, such as material removal rate (MRR) and data acquired from wireless vibration sensors. Subsequently, a non-linear sequential Bayesian analysis is integrated with decision theoretic concepts for detection of CMP process end-point for blanket copper wafers. Using this approach, CMP polishing end-point was detected within a 5% error rate.Next, a non-parametric Bayesian analytical approach is utilized to capture the inherently complex, non-Gaussian, and non-stationary sensor signal patterns observed in CMP process. An evolutionary clustering analysis, called Recurrent Nested Dirichlet Process (RNDP) approach is developed for monitoring CMP process changes using MEMS vibration signals. Using this novel signal analysis approach, process drifts are detected within 20 milliseconds and is assessed to be 3-7 times faster than traditional SPC charts. This is very beneficial to the industry from an application standpoint, because, wafer yield losses will be mitigated to a great extent, if the onset of CMP process drifts can be detected timely and accurately.Lastly, a non-parametric Bayesian modeling approach, termed Dirichlet Process (DP) is combined with a multi-level hierarchical information fusion technique for monitoring of surface finish in UPM process. Using this approach, signal patterns from six different sensors (three axis vibration and force) are integrated based on information fusion theory. It was observed that using experimental UPM sensor data that process decisions based on the multiple sensor information fusion approach were 15%-30% more accurate than the decisions from individual sensors. This will enable more accurate and reliable estimation of process conditions in ultra-precision manufacturing applications

Inventory Locating with Quuppa: The Design and Development of a Real-Time Process Monitoring Web Application Solution

Viasat, Inc. requires precise inventory tracking at their production facility in San Diego, CA. Viasat has installed the Quuppa indoor real-time locating system (RTLS), which it uses to track the real-time position of high-value work-in-process items. In its current state, the system only displays in-the-moment location information, with no available functionality for storing historical data for review, analysis, or visualization. In addition, the data displayed is noisy and prone to significant random error. This paper provides an overview of RTLS methods and technologies, assesses alternative solutions to Viasat’s issue, demonstrates our RTLS integrated web app solution, analyzes its impact, and offers recommendations for future development

Development of process analytical technologies for chromatography based protein purification

While the integration of PAT is already well-advanced in upstream processing of therapeutic proteins, there is still a lack of suitable technologies for real-time process monitoring and control in downstream processing. The development of suitable PAT for chromatography based protein purification was hence the main objective of this thesis

Real-Time Process Monitoring of Micromolding Using Integrated Ultrasonic Sensors

Peer reviewed: YesNRC publication: Ye

Process monitoring and fault detection on a hot-melt extrusion process using in-line Raman spectroscopy and a hybrid soft sensor

We propose a real-time process monitoring and fault detection scheme for a pharmaceutical hot-melt extrusion process producing Paracetamol-Affinisol extrudate. The scheme involves prediction of Paracetamol concentration from two independent sources: a hybrid soft sensor and a Raman-based Partial Least Squares (PLS) calibration model. Both these predictions are used by the developed PCA (Principal Component Analysis) and SPC (Statistical Process Control) monitors to detect process faults and raise alarms. Through real-time extrusion results, it is shown that this two-sensor approach enables the detection of various common process faults which would otherwise remain undetected with a single-sensor monitoring scheme

Development of a Concept for Real-Time Control of Manual Assembly Systems

In contrast to automated machines and installations, manual assembly still lacks real-time process monitoring and possibilities for short-term control and adaptation of assembly systems. This article describes an approach for a concept of real-time control of manual assembly systems. For this purpose, KPIs that can be determined predictively are considered. These indicators enable a standardized and objective process data acquisition and a local process optimization for a higher flexibility and adaptability. In addition to the key figures developed, an approach for the automated acquisition of appropriate process data in manual assembly is described. The further usage of the KPIs and the validation within a real production environment is finally presented

Theoretical framework for real time sub-micron depth monitoring using quantum inline coherent imaging

Inline Coherent Imaging (ICI) is a reliable method for real-time monitoring of various laser processes, including keyhole welding, additive manufacturing, and micromachining. However, the axial resolution is limited to greater than 2 {\mu}m making ICI unsuitable for monitoring submicron processes. Advancements in Quantum Optical Coherence Tomography (QOCT), which uses a Hong-Ou-Mandel (HOM) interferometer, has the potential to address this issue by achieving better than 1 {\mu}m depth resolution. While time-resolved QOCT is slow, Fourier domain QOCT (FD-QOCT) overcomes this limitation, enabling submicron scale real-time process monitoring. Here we review the fundamentals of FD-QOCT and QOCT and propose a Quantum Inline Coherent Imaging system based on FD-QOCT. Using frequency entangled sources available today the system has a theoretical resolution of 0.17 microns, making it suitable for submicron real-time process monitoring.Comment: 12 pages, 8 figure

MONITORING OF INDUSTRIAL PROCESS USING INTELLIGENT TOMOGRAPHY SYSTEM

In any process industries, direct analysis of processes is very important for process control system implementation and improvement. To direct analysison the processes, a real time process monitoring is required. Conventional methods of process monitoring system works by attached sensors inside the pipeline or vessel. Flow influence problem occurs when exist of sensor inside the pipeline. Solution for flow influence problem can be solved by using Electrical Process Tomography system. Electrical Process Tomography system using numbers of thin electrode plate as a sensor, and it is attached inner or outer surface of the pipeline (depend to the type of Tomography technique) to minimize the influence to the flow. This project with title "Monitoring of Industrial Process Using Intelligent Tomography System" will have objectives to study of Electrical Resistance Tomography (ERT) and develop a simple data acquisition system for ERT system. The outcomes of this project will be series of experiment results on practical of ERT technique and a working data acquisition system that able to records acquired data in a PC