Monitoring Of Chemical Processes Research Articles

Optically-Gated Self-Calibrating Nanosensors: Monitoring pH and Metabolic Activity of Living Cells

Quantitative detection of biological and chemical species is critical to numerous areas of medical and life sciences. In this context, information regarding pH is of central importance in multiple areas, from chemical analysis, through biomedical basic studies and medicine, to industry. Therefore, a continuous interest exists in developing new, rapid, miniature, biocompatible and highly sensitive pH sensors for minute fluid volumes. Here, we present a new paradigm in the development of optoelectrical sensing nanodevices with built-in self-calibrating capabilities. The proposed electrical devices, modified with a photoactive switchable molecular recognition layer, can be optically switched between two chemically different states, each having different chemical binding constants and as a consequence affecting the device surface potential at different extents, thus allowing the ratiometric internal calibration of the sensing event. At each point in time, the ratio of the electrical signals measured in the ground and excited states, respectively, allows for the absolute concentration measurement of the molecular species under interest, without the need for electrical calibration of individual devices. Furthermore, we applied these devices for the real-time monitoring of cellular metabolic activity, extra- and intracellularly, as a potential future tool for the performance of basic cell biology studies and high-throughput personalized medicine-oriented research, involving single cells and tissues. This new concept can be readily expanded to the sensing of additional chemical and biological species by the use of additional photoactive switchable receptors. Moreover, this newly demonstrated coupling between surface-confined photoactive molecular species and nanosensing devices could be utilized in the near future in the development of devices of higher complexity for both the simultaneous control and monitoring of chemical and biological processes with nanoscale resolution control.

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

The kernel principal component analysis (KPCA) method employs the first several kernel principal components (KPCs), which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis (SKPCA) is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error δSPE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation directly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure

Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And, the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring.

Photon level chemical classification using digital compressive detection

A key bottleneck to high-speed chemical analysis, including hyperspectral imaging and monitoring of dynamic chemical processes, is the time required to collect and analyze hyperspectral data. Here we describe, both theoretically and experimentally, a means of greatly speeding up the collection of such data using a new digital compressive detection strategy. Our results demonstrate that detecting as few as ∼10 Raman scattered photons (in as little time as ∼30μs) can be sufficient to positively distinguish chemical species. This is achieved by measuring the Raman scattered light intensity transmitted through programmable binary optical filters designed to minimize the error in the chemical classification (or concentration) variables of interest. The theoretical results are implemented and validated using a digital compressive detection instrument that incorporates a 785nm diode excitation laser, digital micromirror spatial light modulator, and photon counting photodiode detector. Samples consisting of pairs of liquids with different degrees of spectral overlap (including benzene/acetone and n-heptane/n-octane) are used to illustrate how the accuracy of the present digital compressive detection method depends on the correlation coefficients of the corresponding spectra. Comparisons of measured and predicted chemical classification score plots, as well as linear and non-linear discriminant analyses, demonstrate that this digital compressive detection strategy is Poisson photon noise limited and outperforms total least squares-based compressive detection with analog filters.

Fabrication of an array of silicon microscales for the monitoring of chemical processes

We present the fabrication and demonstration of a two-dimensional array of scales micromachined in silicon. Each scale consists of a platform suspended by a spring. The mass present on the platform of each scale is measured from a distance by a camera, which is imaging the fringe pattern that arises from optical interference in an air gap under each scale. A diffractive lens at the bottom of the gap separates the fringe signal from unwanted reflections and directs the fringe signal towards the camera. When a mass deflects a scale platform downwards, the gap narrows at the scale centre and the fringe pattern gets tighter. Operation at temperatures as high as 700 °C was demonstrated, which makes the presented device useful for the monitoring of high-temperature chemical processes.

A support vector clustering‐based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data

A new support vector clustering (SVC)‐based probabilistic approach is developed for unsupervised chemical process monitoring and fault classification in this article. The spherical centers and radii of different clusters corresponding to normal and various kinds of faulty operations are estimated in the kernel feature space. Then the geometric distance of the monitored samples to different cluster centers and boundary support vectors are computed so that the distance–ratio–based probabilistic‐like index can be further defined. Thus, the most probable clusters can be assigned to the monitored samples for fault detection and classification. The proposed SVC monitoring approach is applied to two test scenarios in the Tennessee Eastman Chemical process and its results are compared to those of the conventional K‐nearest neighbor Fisher discriminant analysis (KNN‐FDA) and K‐nearest neighbor support vector machine (KNN‐SVM) methods. The result comparison demonstrates the superiority of the SVC‐based probabilistic approach over the traditional KNN‐FDA and KNN‐SVM methods in terms of fault detection and classification accuracies. © 2012 American Institute of Chemical Engineers AIChE J, 59: 407–419, 2013

In-situ monitoring of pharmaceutical and specialty chemicals crystallization processes using endoscopy–stroboscopy and multivariate image analysis

This contribution presents the proof of concept of endoscopy–stroboscopy based in situ low-cost imaging of crystallization processes. This low-cost sensor currently is widely spread in the field of medical diagnosis of human vocal chords and this work presents its application in the context of pharmaceutical and chemical crystallization process monitoring. The model compounds used in this study are the active pharmaceutical ingredient (API) flufenamic acid and citric acid.Since the acquired images are colored, the second aim of the paper is to evaluate the principal component (PCA) based multivariate image analysis (MIA) as a color to gray scale transformation method, and to compare it to the National Television System Committee (NTSC) standard, which uses fixed weights.It was found that particle color, transparency, size and shape related information based on visual inspection is feasible using the endoscope–stroboscope system. The MIA results show that in the case of transparent particles the red, green, blue channels contribute equally to the total information content of color images. The acquisition price of the ATMOS endoscope is similar to that of a laboratory turbidity probe, feature which is relevant for the widespread use of this sensor.

Hidden Markov Model Based Adaptive Independent Component Analysis Approach for Complex Chemical Process Monitoring and Fault Detection

For complex chemical processes with multiple operating conditions and inherent system uncertainty, conventional multivariate process monitoring techniques such as principal component analysis (PCA)...

A new method for thermal analysis

Abstract In this study, we developed the technique of Li+ ion-attachment mass spectrometry (IAMS), a method that has shown promise in the fields of chemical analysis, plasma diagnostics, chemical process monitoring, and thermal analysis. The experimental setup is such that Li+ ions get attached to chemical species (R) by means of intermolecular association reactions to produce (R + Li)+ adduct ions, which are then transferred to a quadrupole mass spectrometer. Recently, an IAMS system became available commercially in a complete form from the Canon Anelva Corp. IAMS has several notable features. It provides only molecular ions, and it permits direct determination of unstable, intermediary, and/or reactive species. Also, it is highly sensitive because it involves ion-molecule reactions. With regard to its applications for thermal analysis, one of its greatest advantages is that it can be used to directly analyze gaseous compounds because it provides mass spectra only of the molecular ions formed by Li+ ion ...

Virtual Reality-based Chemical Process Simulation of Pipeline System

In order to reduce danger and cost in physical chemical process training and testing, this paper designed a distributed virtual reality-based pipeline simulation system which has abilities of chemical process training, monitoring, testing and replaying. After proposing a data-driven simulation framework, this paper presented a virtual reality modeling method for pipeline simulation and a process path calculation method. Then a virtual prototypes pick-up method for device operation and related-information display was further analyzed. With Bernoulli's equation, a mathematical model for constant flowing and instant flowing of fluid in pipeline system are constructed to estimate flowing speed, flux and pressure in real time. Using aforementioned methods, a pipeline simulation system was developed and it was proven to be helpful for chemical process training, design and optimization by practical use.

Feasibility of Monitoring Techniques for Substances Mobilised by CO2 Storage in Geological Formations

When a large volume of CO2 is injected into a geological formation this can lead to the mobilisation of substances of a chemical and physical nature. The purpose of this IEA GHG study[1] was to identify typical substances that could be mobilised during geosequestration and to evaluate potential tools for monitoring these substances. This project reviewed the scientific literature patent applications and industry publications relevant to the current monitoring of chemical and physical processes due to CO2-formation water-rock interactions from the deep subsurface through to the soil/water interface. Four major areas were identified for review: 1– physical effects, including pressure effects or displacement of fluids; 2 – geochemical effects, including dissolution of reservoir and seal rocks, as well as the potential for mobilisation of heavy metals; 3 – shallow/surface effects, potential nutrients/toxic compounds affecting soils and microbial communities, as well as groundwater quality; 4 –capture contaminant effects from coal fired plants and other point source emitters. The various processes have different degrees of impact in the three general monitoring domains: a) injection horizon (depleted hydrocarbon reservoir, saline formation), b) above-zone interval (zone directly overlying the seal of the storage interval), and c) shallow subsurface (potable groundwater aquifers, soil). Understanding these processes and mapping their distribution aids in the identification of potential monitoring tools and facilitates an assessment of their utility in a particular monitoring domain.Some tools already commonly deployed in other industries are highly applicable to the carbon storage industry; for example, downhole pressure gauges from the oil industry and water level loggers from the groundwater industry. In general, geophysical tools were found to be quite a mature method for identifying the presence of gas (hydrocarbons or CO2), but less so for observing mobilised substances and changes in salinity. Tools for measuring trace amounts of hydrocarbons in marine settings are able to be modified in order to be used for monitoring mobilised hydrocarbons entrained in capture emissions, from CO2/source rock interactions or ienhanced oil recovery processes, though many of the tools are not compound specific as yet. The aim of the project is to provide an understanding of the availability of conventional and novel tools for monitoring and verification (M&V) during CO2 injection. Some of these tools have been successfully employed in current carbon capture and storage (CCS) projects or in alternative applications, such as mineral exploration and ecological studies.

Luminescence of Nanodiamond Driven by Atomic Functionalization: Towards Novel Detection Principles

AbstractHigh biocompatibility, variable size ranging from ≈5 nm, stable luminescence from its color centers, and simple carbon chemistry for biomolecule grafting make nanodiamond (ND) particles an attractive alternative to molecular dyes for drug‐delivery. A novel method is presented that can be used for remote monitoring of chemical processes in biological environments based on color changes from photoluminescent (PL) nitrogen‐vacancy (NV) centers in ND. The NV luminescence is driven chemically by alternating the surface chemical potential by interacting atoms and molecules with the diamond surface. Due to the small ND size, the changes of the surface chemical potential modify the electric field profile at the diamond surfaces (i.e., band bending) and intermingle with the electronic NV states. This leads to changes in NV−/NV° PL ratio and allows construction of optical chemo‐biosensors operating in cells, with PL visible in classical confocal microscopes. This phenomenon is demonstrated on single crystal diamond containing engineered NV centers and on oxidized and hydrogenated ND in liquid physiological buffers for variously sized ND particles. Hydrogenation of NDs leads to quenching of luminescence related to negatively charged (NV−) centers and as a result produces color shifts from NV− (638 nm) to neutral NV° (575 nm) luminescence. How the reduction of diamond size increases the magnitude of the NV color shift phenomena is modeled.

Multivariate statistical monitoring of ET o: A new approach for estimation in nearby locations using geographical inputs

The standard equation used to calculate reference evapotranspiration (ET o) requires many parameters that are not available or reliable in most cases. Alternative equations have been developed in the literature relying only on a limited number of climatic records. A different approach is proposed in this work based on exogenous ET o records from locations with similar climatic conditions. Principal components analysis (PCA) is applied to ET o data recorded from 30 weather stations, which allows ET o estimation of past or present missing data when local climatic inputs are not available. This approach resulted more accurate for gap infilling purposes than other tested methods, with average absolute relative errors around 9%. The proposed methodology, which was developed in the 1990s for the monitoring and diagnosis of chemical processes, can only be applied if previous ET o measurements are available. If this is not the case, a new procedure based on principal components regression is proposed to estimate ET o when only local geographical data are available. The resulting models present average absolute relative errors around 10%. The relationships among stations were described by means of a map that can be used for estimation purposes using one or two neighboring stations.

Maintaining the predictive abilities of multivariate calibration models by spectral space transformation

In quantitative on-line/in-line monitoring of chemical and bio-chemical processes using spectroscopic instruments, multivariate calibration models are indispensable for the extraction of chemical information from complex spectroscopic measurements. The development of reliable multivariate calibration models is generally time-consuming and costly. Therefore, once a reliable multivariate calibration model is established, it is expected to be used for an extended period. However, any change in the instrumental response or variations in the measurement conditions can render a multivariate calibration model invalid. In this contribution, a new method, spectral space transformation (SST), has been developed to maintain the predictive abilities of multivariate calibration models when the spectrometer or measurement conditions are altered. SST tries to eliminate the spectral differences induced by the changes in instruments or measurement conditions through the transformation between two spectral spaces spanned by the corresponding spectra of a subset of standardization samples measured on two instruments or under two sets of experimental conditions. The performance of the method has been tested on two data sets comprising NIR and MIR spectra. The experimental results show that SST can achieve satisfactory analyte predictions from spectroscopic measurements subject to spectrometer/probe alteration, when only a few standardization samples are used. Compared with the existing popular methods designed for the same purpose, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS), SST has the advantages of implementation simplicity, wider applicability and better performance in terms of predictive accuracy.

Optical Detection of Charged Biomolecules: Towards Novel Drug Delivery Systems

This paper presents work done on developing optically-traceable intracellular nanodiamond sensors, where the photoluminescence can be changed by a biomolecular attachment/delivery event. Their high biocompatibility, small size and stable luminescence from their color centers make nanodiamond (ND) particles an attractive alternative to molecular dyes for drug-delivery and cell-imaging applications. In our work, we study how surface modification of ND can change the color of ND luminescence (PL). This method can be used as a novel detection tool for remote monitoring of chemical processes in biological systems. Recently, we showed that PL can be driven by atomic functionalization, leading to a change in the color of ND luminescence from red (oxidized ND) to orange (hydrogenated ND). In this work, we show how PL of ND changes similarly when interacting with positively and negatively charged molecules. The effect is demonstrated on fluorinated ND, where the high dipole moment of the C-F bond is favorable for the formation of non-covalent bonds with charged molecules. We model this effect using electrical potential changes at the diamond surface. The final aim of the work is to develop a “smart” optically traceable drug carrier, where the delivery event is optically detectable.

Systematic prediction error correction: A novel strategy for maintaining the predictive abilities of multivariate calibration models

The development of reliable multivariate calibration models for spectroscopic instruments in on-line/in-line monitoring of chemical and bio-chemical processes is generally difficult, time-consuming and costly. Therefore, it is preferable if calibration models can be used for an extended period, without the need to replace them. However, in many process applications, changes in the instrumental response (e.g. owing to a change of spectrometer) or variations in the measurement conditions (e.g. a change in temperature) can cause a multivariate calibration model to become invalid. In this contribution, a new method, systematic prediction error correction (SPEC), has been developed to maintain the predictive abilities of multivariate calibration models when e.g. the spectrometer or measurement conditions are altered. The performance of the method has been tested on two NIR data sets (one with changes in instrumental responses, the other with variations in experimental conditions) and the outcomes compared with those of some popular methods, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). The results show that SPEC achieves satisfactory analyte predictions with significantly lower RMSEP values than global PLS and SBC for both data sets, even when only a few standardization samples are used. Furthermore, SPEC is simple to implement and requires less information than PDS, which offers advantages for applications with limited data.

On the mechanism of charge transfer between neutral and negatively charged nitrogen-vacancy color centers in diamond

ABSTRACTThe presented work aims for the development of optically-traceable intracellular nanodiamond sensors, where photoluminescence can be changed by biomolecular attachment/delivery event. High biocompatibility, small size and stable luminescence from its color centers, makes nanodiamond (ND) particles an attractive alternative to molecular dyes for drug-delivery and cell-imaging applications. In our work we study how the surface modification of ND can change ND luminescence spectra. This method can be used as a novel detection tool for remote monitoring of chemical processes in biological systems. We discuss photoluminescence (PL) spectra of oxidized and hydrogenated ND and a single crystal diamond, containing engineered NV centers. The hydrogenation of ND leads to quenching of NV- related luminescence and a PL shift due to changing of occupation from NV- to NV0 states. We model this effect using electrical potential changes at the diamond surface.

Scanning Electron Microscopy for in Situ Monitoring of Semiconductor−Liquid Interfacial Processes: Electron Assisted Reduction of Ag Ions from Aqueous Solution on the Surface of TiO2 Rutile Nanowire

The combination of scanning electron microscopy and environmental cell equipped with electron transparent but yet gas/liquid impenetrable membrane was used for in situ, real time monitoring of chemical processes taking place at the surfaces of individual fully hydrated metal oxide nanostructures at nanoscopic scale. Similar to the photoreduction process, it was observed that secondary electrons generated in TiO2 nanowire by the primary electron beam lead to electron mediated Ag deposition from AgNO3 solution. The formation and growth of Ag nanoparticles, nanowires, and dendrimeric structures at the TiO2−liquid interface was investigated. The demonstrated approach has immense potential for real time imaging and rapid, in vivo compositional analysis of the interfacial chemical processes relevant to (photo)catalysis, batteries, fuel cells, and environmental remediation.

Localized Fisher discriminant analysis based complex chemical process monitoring

Complex chemical process is often corrupted with various types of faults and the fault-free training data may not be available to build the normal operation model. Therefore, the supervised monitoring methods such as principal component analysis (PCA), partial least squares (PLS), and independent component analysis (ICA) are not applicable in such situations. On the other hand, the traditional unsupervised algorithms like Fisher discriminant analysis (FDA) may not take into account the multimodality within the abnormal data and thus their capability of fault detection and classification can be significantly degraded. In this study, a novel localized Fisher discriminant analysis (LFDA) based process monitoring approach is proposed to monitor the processes containing multiple types of steady-state or dynamic faults. The stationary testing and Gaussian mixture model are integrated with LFDA to remove any nonstationarity and isolate the normal and multiple faulty clusters during the preprocessing steps. Then the localized between-class and within-class scatter mattress are computed for the generalized eigenvalue decomposition to extract the localized Fisher discriminant directions that can not only separate the normal and faulty data with maximized margin but also preserve the multimodality within the multiple faulty clusters. In this way, different types of process faults can be well classified using the discriminant function index. The proposed LFDA monitoring approach is applied to the Tennessee Eastman process and compared with the traditional FDA method. The monitoring results in three different test scenarios demonstrate the superiority of the LFDA approach in detecting and classifying multiple types of faults with high accuracy and sensitivity. © 2010 American Institute of Chemical Engineers AIChE J, 2011

A combined single photon ionization and photoelectron ionization source for orthogonal acceleration time-of-flight mass spectrometer

A novel ion source has been introduced in the present study, which combines the characteristics of single photon ionization (SPI) and photoelectron ionization (PEI). The VUV photons for SPI were generated by a commercial krypton discharge lamp (10.6 eV), and the photoelectrons for electron ionization (EI) were produced through photoemission from a stainless-steel skimmer. The energy of photoelectrons can be well controlled by adjusting the voltages applied on the skimmer. The ionization methods, SPI (SPI mode) and SPI combined photoelectron ionization (SPI–PEI mode), can be rapidly switched. Benefited from the higher working pressure in the ion source, the achieved detection limit of benzene and SO 2 were 0.1 ppmV and 20 ppmV, respectively. The experimental results show the combined ion source has the potential in chemical process and environmental monitoring.