Online monitoring of a bioprocess based on a multi‐analyser system and multivariate statistical process modelling

Multivariate statistical process control (MSPC) was for the first time applied to analyse data from a bioprocess on‐line multi‐analyser system consisting of an electronic nose (EN), a near‐infrared spectroscope (NIRS), a mass spectrometer (MS) and standard bioreactor probes. One hundred and fifty sensor signals from the electronic nose, 1050 wavelength signals from the NIRS, carbon dioxide evolution rate calculated from mass spectrometer signals and standard bioreactor data (eg amount of substrate fed) were interrogated for their ability to model a bioprocess using MSPC. The models obtained were validated on a recombinant Escherichia coli fed‐batch process for tryptophan production. Limiting trajectories were defined in the MSPC models for warning, action, and process experience with respect to biomass and tryptophan concentrations. The results showed the capacity and robustness of MSPC models for monitoring with multi‐analysers and allowed a comparison of the different analysers' suitability for this kind of data processing. Furthermore, the results demonstrate that MSPC models provide a functional and versatile framework for coping with large information flows and are also suited to a variety of other bioprocessing monitoring and control tasks. © 2002 Society of Chemical Industry     

Continuous manufacturing: Is the process mean stationary?

The statistical framework to systematically detect mean stationarity in the context of continuous manufacturing is described in this article. The methods presented in this article use econometric and financial time‐series analysis concepts in the form of unit‐root and stationarity hypothesis tests. The tests under discussion are the augmented Dickey‐Fuller, Philips‐Perron, Leybourne‐McCabe, and Kwiatkowski‐Phillips‐Schmidt‐Shin. These hypothesis tests are evaluated on data generated by a focused‐beam reflectance measurement sensor implemented on‐line in a continuous plug‐flow crystallizer. This contribution has shown that the hypothesis tests can be used to detect steady‐state conditions on‐line in a plug‐flow crystallizer. Furthermore, this econometric framework can be used as a mean stationarity “certificate” of collected samples to document that the process was mean stationary during the sampling. The statistical framework described in this article can be applied to any continuously operated unit operation or sensor measurement. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2426–2437, 2018     

A Surface Plasmon Resonance Optical Fibre Sensor for Testing Detergent Cleaning Efficiency

The objective of this paper is to report a feasibility study on the use of optical fibre and surface plasmon resonance (SPR) for determining detergent efficiency. The concept presented comprises a miniaturised dip‐sensor for enabling automated on‐line testing. In this way, the effect of formulation, concentration and temperature on the cleaning behavior of various surfactants and commercial cleaners is investigated. For this purpose, the decladded core of an optical fibre was sputtered with gold and afterwards coated with defined layers of stearic acid or animal fat to obtain a kind of model soil. The deposition of stearic acid was performed using a Langmuir–Blodgett through, and the sensor followed on‐line the deposition of the respective monolayers by UV/VIS spectroscopy, appearing as a distinct and constant shift in wavelength. Moreover, functional coatings were applied above the gold layer to achieve a variation of the hydrophilicity of the sensor surface. The SPR sensor proved to be easy to use, accurate and flexible. It offers a new solution that could replace the existing methods for detergency sensing and with a customised design it could be a useful industry tool since the small size of the dip sensor promises massive testing. The experiment also showed that functionalising the sensing zone could act as a way to mimic the potential substrates for cleaning.     

诺西肽发酵过程中的分阶段软测量建模

诺西肽发酵过程中关键生化参数难以在线测量,给控制与优化带来困难.针对这一问题,利用软测量技术来实现关键生化参数的在线估计,并提出了一种分阶段软测量建模方法.首先以分阶段的诺西肽发酵过程非结构模型为基础,根据隐函数存在定理进行辅助变量的合理选择;然后利用模糊c均值聚类算法将建模数据按其所属阶段的不同进行分类,并利用神经网...     

ATR‐UV monitoring of methyl methacrylate miniemulsion polymerization for determination of monomer conversion

Attenuated Total Reflection (ATR) UV spectroscopy has been used to monitor monomer conversion in methyl methacrylate miniemulsion polymerization. It was found that the vinylic groups of methyl methacrylate strongly absorb the UV light with a maximum absorption at 225 nm. This absorption peak decreases as monomer is converted to polymer. The polymer has a strong absorption at a lower UV region. The results from this feasibility study indicate that ATR‐UV sensor technique has a great potential to be used for on‐line or in‐line process monitoring in emulsion and miniemulsion polymerization. With a partial least square (PLS) calibration model, very good prediction the monomer conversion was obtained. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 99: 1471–1475, 2006     

Application of ultrasound in the determination of fundamental extrusion performance: Residence time distribution measurement

By means of new probe design and rapid data acquisition, we have succeeded in in‐line ultrasonic monitoring of residence time distribution (RTD) at the melting, mixing, and pumping zones as well as at the die exit of a Werner & Pfleiderer 30‐mm twin‐screw extruder by mounting the ultrasonic probes on the extruder barrel over the screw elements and at the die. The experimental systems were LDPE, CaCO₃‐filled LDPE, and a Kraton/LDPE blend. The ultrasonic data at each of the extruder functional zones are presented. The ultrasonic results have been used to evaluate an opical RTD measurement method by using an optical sensor side by side with one ultrasonic probe at the mixing zone of the extruder. The comparison of the ultrasonic and optical results has shown that the presented ultrasonic technique could be a good complement to the optical technique in the monitoring and understanding of RTD during polymer extrusion processes.     

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

State estimation from plant measurements plays an important role in advanced monitoring and control technologies, especially for chemical processes with nonlinear dynamics and significant levels of process and sensor noise. Several types of state estimators have been shown to provide high‐quality estimates that are robust to significant process disturbances and model errors. These estimators require a dynamic model of the process, including the statistics of the stochastic disturbances affecting the states and measurements. The goal of this article is to introduce a design method for nonlinear state estimation including the following steps: (i) nonlinear process model selection, (ii) stochastic disturbance model selection, (iii) covariance identification from operating data, and (iv) estimator selection and implementation. Results on the implementation of this design method in nonlinear examples (CSTR and large dimensional polymerization process) show that the linear time‐varying autocovariance least‐squares technique accurately estimates the noise covariances for the examples analyzed, providing a good set of such covariances for the state estimators implemented. On the estimation implementation, a case study of a chemical reactor demonstrates the better capabilities of MHE when compared with the extended Kalman filter. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

In‐line monitoring of polyethylene density using near infrared (NIR) spectroscopy

Density and melt index are two key properties in grading commercial polyolefin polymers. For quality assurance, these properties must be controlled as accurately as possible in the production plant. However, the lack of suitable in‐line sensors for these properties makes feedback quality control difficult. In this study, an in‐line density sensor using near infrared (NIR) spectroscopy is developed. The NIR spectra of molten polyethylene in flow are collected by a fiber‐optic device attached to a single screw extruder. By the ratio of the absorption intensity at 1170 nm to that at 1213 nm, the densities of 14 grades of polyethylene were successfully measured. The results were very promising for quality control in the polyethylene production process.     

Model Optimization for the Dynamic Simulation of Reactive Absorption Processes

The optimal design of reactive separations is impossible without reliable process models. Especially for the dynamic simulation and the model‐based control of complex reactive absorption processes the model development leads to a contradiction between the required model accuracy to reflect the process complexity and the feasibility of process simulations regarding the computation time. In this respect, we have developed a new rigorous dynamic two‐phase model based on the two‐film theory as a first step, which takes into account the influence of chemical reactions and additional driving forces in electrolyte systems on mass transfer considering thermodynamic nonidealities as well as the impact of column internals on the process hydrodynamics. For a model optimization, we have performed an analysis of different model approaches for complicated industrial absorption processes and determined an appropriate model complexity. Based on results of sensitivity studies, we have accomplished different model modifications leading to a stabilization of the numerical solution without affecting the good agreement between simulation results and the experimental data. This time‐optimized model can be considered superior as compared to previous approaches and facilitates for the first time a rigorous dynamic simulation of entire reactive absorption columns and the application within an on‐line process control system.     

Designing and Testing a Chemical Demulsifier Dosage Controller in a Crude Oil Desalting Plant: An Artificial Intelligence‐Based Network Approach

The aim of this paper is to present an artificial neural network (ANN) controller trained on a historical data set that covers a wide operating range of the fundamental parameters that affect the demulsifier dosage in a crude oil desalting process. The designed controller was tested and implemented on‐line in a gas‐oil separation plant. The results indicate that the current control strategy overinjects chemical demulsifier into the desalting process whereas the proposed ANN controller predicts a lower demulsifier dosage while keeping the salt content within its specification targets. Since an on‐line salt analyzer is not available in the desalting plant, an ANN based on historical measurements of the salt content in the desalting process was also developed. The results show that the predictions made by this ANN controller can be used as an on‐line strategy to predict and control the salt concentration in the treated oil.     

Dielectric spectroscopy for polymer melt composition measurements

Laboratory and in‐line dielectric spectroscopy studies of molten polymers revealed the ability to make composition measurements of the comonomer concentration along the polymer chains. The dielectric permittivity or capacitance was found to be proportional to the polymer sidechain composition. The length of the polar sidegroup dictated the optimum measurement frequency; i.e., a short sidechain, such as the C‐Cl bond in chlorosulfonated polyethylene, required a high measurement frequency (short relaxation time) due to the rapid mobility of the sidechain, whereas a longer sidechain, such as the vinyl ether sidegroup in Nafion®, required a much lower frequency (longer relaxation time) to orient the sidechain to a measurable extent. For in‐line process studies, a new cylindrical, interdigitated dielectric sensor and associated electronics were developed to make measurements on molten polymers at the exit of an extruder up to 400°C and 3000 lbs/in². In‐line studies of molten polymers revealed a direct relationship between the dielectric signal and the polymer comonomer composition. The sensor represents a non‐invasive, real‐time process composition measurement and can be integrated to provide closed‐loop process control.     

Online simulation‐based process control for injection molding

An online numerical simulation is presented that is capable of predicting state variables such as flow rate, melt temperature, shear rate, and melt viscosity by using real time data from a nozzle pressure sensor. The simulation solves the non‐Newtonian nonisothermal polymer flow into multicavity tools while executing rapidly enough for real time process control. Numerical accuracy and stability were first validated offline by comparing the online simulation to results obtained from a commercial mold filling simulation. Simulation‐based process control was then demonstrated by transferring a molding machine from fill to pack‐based on the predicted flow front position. The simulation‐based controller dynamically determined the appropriate transfer position for each part and transferred the machine at the correct time, thereby eliminating flash. The simulation, however, did increase process variability slightly due to delay times associated with the controller‐machine interface. A full factorial design of experiments (DOE) was performed varying injection velocity, mold temperature, and melt temperature. Results show that while the simulation dynamically adjusted the process on a part‐by‐part basis, it did not fully account for the process changes. Accuracy could potentially be improved by incorporating data from additional process sensors, by developing adaptive viscosity models, and by accounting for the melt compressibility. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers     

Error‐triggered on‐line model identification for model‐based feedback control

In industry, it may be difficult in many applications to obtain a first‐principles model of the process, in which case a linear empirical model constructed using process data may be used in the design of a feedback controller. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state‐space and may also perform poorly when significant plant variations and disturbances occur. In the present work, an error‐triggered on‐line model identification approach is introduced for closed‐loop systems under model‐based feedback control strategies. The linear models are re‐identified on‐line when significant prediction errors occur. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re‐identification on‐line when necessary. The proposed approach is demonstrated through two chemical process examples using a model‐based feedback control strategy termed Lyapunov‐based economic model predictive control (LEMPC). The chemical process examples illustrate that the proposed error‐triggered on‐line model identification strategy can be used to obtain more accurate state predictions to improve process economics while maintaining closed‐loop stability of the process under LEMPC. © 2016 American Institute of Chemical Engineers AIChE J, 63: 949–966, 2017     

相位法含水率测量传感器的模型研究

相位法原油含水率测量方法是基于同轴线传感器的一种测量方法,同轴线传感器的结构与参数对测量准确性有着重要影响。利用ANSYS HFSS电磁仿真软件研究同轴线传感器的设计优化方法,分析同轴线内导体的长度对传感器性能的影响,依据仿真结果,确定传感器的优化参数。仿真结果证明了相位法原油含水率测量的有效性和可行性。     

On‐line NIR sensing of CO2 concentration for polymer extrusion foaming processes

An on‐line sensor using near infrared (NIR) spectroscopy is developed for monitoring CO₂ concentration in polymeric extrusion foaming processes. NIR absorption spectra are acquired by a probe installed at the foaming extruder die. The calibration curve relating the absorbance spectrum at 2019 nm to the dissolved gas concentration is derived so as to infer dissolved CO₂ gas concentration on‐line from measured NIR spectra. Experimental results show the developed on‐line NIR sensor can successfully estimate dissolved CO₂ concentration in the molten polymer and illustrate that the developed NIR sensing technique is among the more promising methods for quality control of polymeric extrusion foaming processes.     

Requirements capture for colour information for design professionals

This article presents the results of a study that investigates the status of colour information use in the design process and generates ideas for a colour tool. Face‐to‐face interviews with senior designers and brand managers from the packaging and branding fields were conducted as the primary data collection method. The results are categorized into six topics: colour decision, types of colour information considered to be important in the design process, reasons for considering colour information important in the design process, current use of colour information, design professionals' preferences for existing colour tool types and data types and suggestions for a colour tool. It is concluded that there are problems with existing colour resources and tools regarding their availability and usefulness; there is a strong demand for a colour tool in the packaging design and branding processes. The insight from this work will help researchers, design professionals and colour tool developers to make informed decisions on the areas on which they should focus, how they should do so and why. This will facilitate better provisions and uptake of useful colour information for design professionals in the design process and strategy fields.     

A model‐based virtual sensing approach for the injection molding process

This paper proposes a new virtual sensing approach for on‐line monitoring and regulating process variables of the injection molding process. Based on the nonlinear observer theory, virtual sensors estimate process behavior using easily obtained measurements of machine variables by exploiting their dynamic interaction. A nozzle pressure virtual sensor during the so‐called “nozzle resistance test” was developed. Results of experimental evaluation on a commercial injection molding machine establish the feasibility of the proposed virtual sensing approach. Furthermore, a sensitivity analysis indicates that the proposed virtual sensor delivered consistently better and more robust performance against parametric uncertainties than simple open‐loop model prediction. Polym. Eng. Sci. 44:1605–1614, 2004. © 2004 Society of Plastics Engineers.     

Multipurpose carbon fiber sensor design for analysis and monitoring of the resin transfer molding of polymer composites

The resin transfer molding (RTM) process is taking an ever‐growing place among the manufacturing technologies of polymer composite parts because of its numerous advantages. However, consistent production of high‐quality parts is difficult to achieve and requires better understanding of the process and good control of the raw materials. Part‐to‐part variations are inevitable as a result of uncarefully controlled molding environments and unidentifiable or undetected disturbances that cannot be completely eliminated or accounted for. Despite of many efforts to understand and model the fundamental physical and chemical behavior of materials during processing, there is no reliable system able of predicting the optimum processing parameters to manufacture high‐quality parts in a productive way. In this context, this work aims to develop systems allowing the monitoring of the whole RTM process (from preforming to resin curing) and that are reliable, cheap, and easy to use on production line. We have chosen to investigate the potential of the electric and dielectric carbon fiber sensors, which have already proved to be suitable for in service damage monitoring and preventive maintenance without any integration issues. However, the development of the continuous electric sensor has been limited by the polarization of the resin under the direct current. The flow front and the cure monitoring of the resin has been achieved with the dielectric sensor, energized by an alternative current preventing the polarization. Additionally, the ability of this carbon fiber sensor to evaluate the thickness of dry reinforcements and to measure online the actual unsaturated permeability of reinforcements has been demonstrated. POLYM. COMPOS., 26:717–730, 2005. © 2005 Society of Plastics Engineers     

Detection,Diagnosis and Root Cause Analysis of Sheet‐Break in a Pulp and Paper Mill with Economic Impact Analysis

Sheet‐break is a long standing problem in the pulp and paper industry. This study is concerned with the analysis of process data to diagnose causes of sheet‐breaks and therefore significant down times. PCA was used to model the process and a combined index based on the Hotelling's T² and Squared Prediction Error (SPE) was developed as a sheet‐break detection indicator. As the process is subject to external disturbances, changes and frequent interruptions, pre‐processing of the data played an important role in getting consistent results. We used several novel techniques for data selection, scaling and modelling. The models were validated using a large validation data set with known fault conditions. The developed model, data visualization tool and engineering judgement was used for off‐line diagnosis of root causes of sheet‐breaks. Several operational changes were recommended and implemented on the process resulting in significantly reduced sheet‐breaks. Key Performance Indicators calculated before and after the changes shows the significant economic gain as a result of this 'data‐mining' project.