Slug length estimation for gas-liquid slug flow in T-shaped microdevices with liquid film

13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2022, Busan, Republic of Korea, 14–17 June 2022To realize stable long-term operation of microdevices with gas-liquid slug flow, the slug lengths have to be monitored and controlled, because they influence mass transfer performance. In this study, an experimental investigation was carried out to analyze the gas-liquid slug flow in a T-shaped microdevice with a liquid film. The experimental result showed that the pressures in gas and liquid feeding tubes oscillate periodically along the formation of a pair of gas and liquid slugs. Then, the correlation equation between the liquid film thickness and the number of capillaries was identified on the basis of the experimental data. Based on these results, a method for estimating slug lengths and liquid film thickness from measurements of feed pressure and feed flowrate was developed. The developed method is non-invasive and does not affect slug formation or the manner of gas-liquid slug flow. Its effectiveness was assessed through an experimental case study, and the relative root mean square errors of estimated slug lengths were within 8.5%. The result show that the developed method can be applied to the monitoring of slug lengths

Development of correlation-based clustering method and its application to software sensing

Abstract The individuality of production devices should be taken into account when soft-sensors are designed for parallelized devices. Since it is expressed as differences of the correlation among measured variables, it is useful to cluster samples on the basis of the correlation among variables for adopting a multi-model approach. In addition, changes in process characteristics can be coped with in the same way. In the present work, a new clustering method, referred to as NC-spectral clustering, is proposed by integrating the nearest correlation (NC) method and spectral clustering. Spectral clustering is a graph partitioning method that can be used for sample classification when an affinity matrix of a weighted graph is given. The NC method can detect samples that are similar to the query from the viewpoint of the correlation without a teacher signal. In the proposed method, the NC method is used for constructing the weighted graph that expresses the correlation-based similarities between samples and the constructed graph is partitioned by using spectral clustering. In addition, a new soft-sensor design method is proposed on the basis of the proposed NCspectral clustering. The usefulness of the proposed methods is demonstrated through a numerical example and a case study of parallelized batch processes. The performance of the proposed correlation-based method is better than that of the conventional distance-based methods

Formulas to Estimate Appropriate Surgical Amounts of Unilateral Recession-Resection in Intermittent Exotropia with Distance-Near Disparity

The purpose of this study was to derive new formulas to provide an optimal surgical procedure and optimal amount of recession-resection (RR) surgery in intermittent exotropia (IXT) with a disparity in angle of deviation depending on the fixation distance. The records of 117 consecutive patients with IXT who underwent RR surgery between March 2008 and December 2011 at Okayama University Hospital were retrospectively examined. Multivariable linear regression analysis was performed using the observed corrective angle of deviation at distance or near fixation as the dependent variable, and amounts of lateral rectus muscle (LR) recession (mm) and medial rectus muscle (MR) resection, and age at surgery (years) as independent variables. Two simultaneous formulas were derived: corrective angle of deviation at distance fixation (°)=1.8×recession (mm)+1.6× resection (mm)+0.15×age (years)–6.6, and corrective angle at near fixation (°)=1.5×recession (mm)+1.7× resection (mm)+0.18×age (years)–3.8. Comparisons of coefficient values of the formulas between distance and near fixation revealed that LR recession was more affected by the corrective angle in distance than near fixation. MR resection was more affected at near than distance fixation. We found that our new formulas estimated the appropriate amount of unilateral RR surgery

Differences in the Stability and Amount of Postoperative Exodrift with Age after Unilateral Lateral Rectus Muscle Recession and Medial Rectus Muscle Resection of Intermittent Exotropia

We investigated variances in the stability and amount of postoperative exodrift among age groups of intermittent exotropia (XPT) patients who underwent unilateral lateral rectus muscle recession and medial rectus muscle resection. We analyzed the cases of 110 consecutive patients who underwent the surgery in 2004-2011, dividing the patients into groups by their age at surgery: <10, 10-19, and ≥20 years. We performed a regression analysis (dependent variable: postoperative exodrift (°); independent variable: number of days post-surgery) using the formula of curve lines. When the tangent line slope was = 0.01 (°/days) for each group, we defined the numbers of days until alignment became stable as the ‘stable days.’ We evaluated the between-group differences in the amount of exodrift calculated for the stable days. The coefficients and coefficients of determination for the fitting curves were: <10 year group: f(x)=12.2 (1−e−0.0183x) (r2=0.588, p<0.05); 10-19 year group: f(x)=10.0 (1−e−0.0178x) (r2=0.453, p<0.05); ≥20 year group: f(x)=3.40 (1−e−0.0382x) (r2=0.217, p<0.05). There were 389 , 388, and 153 stable days, and the estimated postoperative exodrift with long-term follow-up was 11.5±3.7°, 9.3±4.4°, and 4.1±3.6° for the < 10 year, 10-19 year, and ≥ 20 year groups, respectively (≥20 year vs. other 2 groups, p<0.05). Longer periods and more postoperative exodrift were associated with younger age at surgery. The postoperative evaluation was approx. ≥ 1 year post-surgery in patients aged < 20. These findings may contribute to evaluating XPT’s success rate and prognoses

中間タンクを有するバッチプロセスの最適設計と運転計画

京都大学0048新制・課程博士工学博士甲第3182号工博第867号新制||工||607(附属図書館)UT51-59-H104京都大学大学院工学研究科化学工学専攻(主査)教授 髙松 武一郎, 教授 江口 彌, 教授 橋本 健治学位規則第5条第1項該当Kyoto UniversityDFA

Development of correlation-based clustering method and its application to software sensing

The individuality of production devices should be taken into account when soft-sensors are designed for parallelized devices. Since it is expressed as differences of the correlation among measured variables, it is useful to cluster samples on the basis of the correlation among variables for adopting a multi-model approach. In addition, changes in process characteristics can be coped with in the same way. In the present work, a new clustering method, referred to as NC-spectral clustering, is proposed by integrating the nearest correlation (NC) method and spectral clustering. Spectral clustering is a graph partitioning method that can be used for sample classification when an affinity matrix of a weighted graph is given. The NC method can detect samples that are similar to the query from the viewpoint of the correlation without a teacher signal. In the proposed method, the NC method is used for constructing the weighted graph that expresses the correlation-based similarities between samples and the constructed graph is partitioned by using spectral clustering. In addition, a new soft-sensor design method is proposed on the basis of the proposed NC-spectral clustering. The usefulness of the proposed methods is demonstrated through a numerical example and a case study of parallelized batch processes. The performance of the proposed correlation-based method is better than that of the conventional distance-based methods

Two-stage subspace identification for softsensor design and disturbance estimation

Softsensors or virtual sensors are key technologies in industry because important variables such as product quality are not always measured on-line. In the present work, two-stage subspace identification (SSID) is proposed to develop highly accurate softsensors that can take into account the influence of unmeasured disturbances on estimated key variables explicitly. The proposed two-stage SSID method can estimate unmeasured disturbances without the assumptions that the conventional Kalman filtering technique must make. Therefore, it can outperform the Kalman filtering technique when innovations are not Gaussian white noises or the characteristics of disturbances do not stay constant with time. The superiority of the proposed method over the conventional methods is demonstrated through numerical examples and application to an industrial ethylene fractionator

Development of correlation-based pattern recognition algorithm and adaptive soft-sensor design

Although soft-sensors have been used for estimating product quality, they do not always function well due to not only changes in process characteristics but also the individual difference of production devices. Correlation-based Just-In-Time (CoJIT) modeling has been proposed to cope with such changes in process characteristics; however it cannot deal with the individual difference. In the present work, a new pattern recognition method, referred to as the nearest correlation (NC) method is proposed to cope with the individual difference. The proposed NC method is integrated with CoJIT modeling. The advantages of the proposed methods are demonstrated through a case study

Input variable scaling for statistical modeling

Input variable scaling is one of the most important steps in statistical modeling. However, it has not been actively investigated, and autoscaling is mostly used. This paper proposes two input variable scaling methods for improving the accuracy of soft sensors. One method statistically derives the input variable scaling factors; the other one uses spectroscopic data of a material whose content is estimated by the soft sensor. The proposed methods can determine the scales of the input variables based on their importance in output estimation. Thus, it can reduce the negative effects of input variables which are not related to an output variable. The effectiveness of the proposed methods was confirmed through a numerical example and industrial applications to a pharmaceutical and a distillation processes. In the industrial applications, the proposed methods improved the estimation accuracy by up to 63% compared to conventional methods such as autoscaling with input variable selection

Operation policy for micro chemical plants with external numbering-up structure

Microreaction Technology IMRET 9: Proceedings of the Ninth International Conference on Microreaction Technology - IMRET9 Special IssueOne of the critical operational issues of micro chemical plants with external numbering-up structure is to keep a uniform flow distribution among parallelized microdevices even when blockage occurs in one or more microdevice. Since it is not practical to install flow controllers in all the microdevices, a simple and effective operation policy against blockage occurrence needs to be developed. In this research, micro chemical plants having four or eight parallelized microdevices are constructed to analyze the influence of blockage on the flow distribution among the parallelized microdevices. The numerical and experimental results show that pressure drop control is superior to total flow control. In addition, two control structures based on pressure drop control, pumping pressure control and pressure drop control over the parallelized section, are investigated. It is clarified that the latter control structure enables us to successfully keep the flow rate in each unblocked microdevice at a normal level when blockage occurs. Pressure drop control over the parallelized section is applied to a micro chemical plant having four parallelized micro heat exchangers, and its validity is demonstrated