scholarly journals Evaluation of Process Control Schemes for Sour Water Strippers in Petroleum Refining

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 363
Author(s):  
Chii-Dong Ho ◽  
Yih-Hang Chen ◽  
Chao-Min Chang ◽  
Hsuan Chang
Keyword(s):  
Feedback Control ◽  
Prediction Models ◽  
Feedforward Control ◽  
Absolute Error ◽  
Dynamic Responses ◽  
Control Schemes ◽  
Online Measurements ◽  
Measurement Delay ◽  
Water Feed ◽  
Sour Water

For the sour water strippers in petroleum refinery plants, three prediction models were developed first, including the estimators of sour water feed concentrations using convenient online measurements, the minimum reboiler duty and the corresponding internal temperature at a specific location (Tstage,29). Feedforward control schemes were developed based on these prediction models. Four categories of control schemes, including feedforward, feedback, feedback with external reset, and feedforward-feedback, were proposed and evaluated by the rigorous dynamic simulation model of the sour water stripper for their dynamic responses to the sour water feed stream disturbances. The comparison of control performance, in terms of the settling time, integrated absolute error (IAE) of the NH3 concentration of the stripped sour water and IAE of the specific reboiler duty, reveals that FFT (feedforward control of Tstage,29) and FBA-DT3 (feedback control with 3 min concentration measurement delay) are the best control schemes. The second-best control scheme is FBAT (cascade feedback control of concentration with temperature).

Feedforward Control of Solar Thermal Power Plants

1997 ◽  
Vol 119 (1) ◽  
pp. 52-60 ◽  
Author(s):  
A. Meaburn ◽  
F. M. Hughes
Keyword(s):  
Feedback Control ◽  
Solar Collector ◽  
Power Plants ◽  
Feedforward Control ◽  
Thermal Power ◽  
Control Loop ◽  
Parabolic Trough ◽  
Solar Thermal Power ◽  
Control Schemes ◽  
Feedforward Controller

In recent years the problem of controlling the temperature of oil leaving an array of parabolic trough collectors has received much attention. The control schemes developed have in general utilized a feedback control loop combined with feedforward compensation. The majority of the published papers place the emphasis almost entirely on the design of the feedback control loop. Little or no attention has been paid to issues involved in the design of the feedforward controller. This paper seeks to redress this imbalance by concentrating upon the design and development of a feedforward controller for the ACUREX distributed solar collector field at the Plataforma Solar de Almeria. Different methods of combining feedback and feedforward will be assessed and experimental results will be presented in order to support any theoretical observations made.

Perfectly Matched Feedback Control and Its Integrated Design for Multiaxis Motion Systems

2004 ◽  
Vol 126 (3) ◽  
pp. 547-557 ◽  
Author(s):  
Syh-Shiuh Yeh ◽  
Pau-Lo Hsu
Keyword(s):  
Feedback Control ◽  
High Speed ◽  
Feedforward Control ◽  
Phase Error ◽  
Integrated Design ◽  
Cnc Machining ◽  
Dynamic Responses ◽  
Tracking Accuracy ◽  
Control Loops ◽  
Contouring Accuracy

For motion systems with multiple axes, the approach of matched direct current gains has been generally adopted to improve contouring accuracy under low-speed operations. To achieve high-speed and high-precision motion in modern manufacturing, a perfectly matched feedback control (PMFBC) design for multiaxis motion systems is proposed in this paper. By applying stable pole-zero cancellation and including complementary zeros for uncancelled zeros for all axes, matched dynamic responses across the whole frequency range for all axes are achieved. Thus, contouring accuracy for multiaxis systems is guaranteed for the basic feedback loops. In real applications, the modeling error is unavoidable and the degradation and limitations of the model-based PMFBC exist. Therefore, a newly designed digital disturbance observer is proposed to be included in the proposed PMFBC structure for each axis to compensate for undesirable nonlinearity and disturbances to maintain the matched dynamics among all axes for the PMFBC design. Furthermore, the feedforward control loops zero phase error tracking controller are employed to reduce tracking errors. Experimental results on a three-axis CNC machining center indicate that both contouring accuracy and tracking accuracy are achieved by applying the present PMFBC design.

Use of vent stack temperature as a feedforward variable for dissolver total titratable alkali (TTA) control

TAPPI Journal ◽  
2018 ◽  
Vol 17 (05) ◽  
pp. 261-269
Author(s):  
Wei Ren ◽  
Brennan Dubord ◽  
Jason Johnson ◽  
Bruce Allison
Keyword(s):  
Feedback Control ◽  
Current Practice ◽  
Feedforward Control ◽  
Control Variable ◽  
Indirect Measurement ◽  
Tight Control ◽  
Transfer Lines ◽  
Green Liquor ◽  
Process Dynamics ◽  
Downstream Processes

Tight control of raw green liquor total titratable alkali (TTA) may be considered an important first step towards improving the overall economic performance of the causticizing process. Dissolving tank control is made difficult by the fact that the unknown smelt flow is highly variable and subject to runoff. High TTA variability negatively impacts operational costs through increased scaling in the dissolver and transfer lines, increased deadload in the liquor cycle, under- and over-liming, increased energy consumption, and increased maintenance. Current practice is to use feedback control to regulate the TTA to a target value through manipulation of weak wash flow while simultaneously keeping dissolver density within acceptable limits. Unfortunately, the amount of variability reduction that can be achieved by feedback control alone is fundamentally limited by the process dynamics. One way to improve upon the situation would be to measure the smelt flow and use it as a feedforward control variable. Direct measurement of smelt flow is not yet possible. The use of an indirect measurement, the dissolver vent stack temperature, is investigated in this paper as a surrogate feedforward variable for dissolving tank TTA control. Mill trials indicate that significant variability reduction in the raw green liquor TTA is possible and that the control improvements carry through to the downstream processes.

scholarly journals Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

2021 ◽  
Vol 79 (4) ◽  
pp. 1533-1546
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A. Strange ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Assessment ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning

Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective: To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods: Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results: Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion: Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

scholarly journals Model-Based Design Approach to Improve Performance Characteristics of Hydrostatic Bearing Using Multivariable Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 388
Author(s):  
Waheed Ur Rehman ◽  
Xinhua Wang ◽  
Yiqi Cheng ◽  
Yingchun Chen ◽  
Hasan Shahzad ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Feedback Control ◽  
External Load ◽  
Sliding Mode ◽  
Feedforward Control ◽  
Spindle Speed ◽  
Performance Criteria ◽  
Model Based ◽  
Hydrostatic Bearings ◽  
Robust Control Design

Research in the field of tribo-mechatronics has been gaining popularity in recent decades. The objective of the current research is to improve static/dynamics characteristics of hydrostatic bearings. Hydrostatic bearings always work in harsh environmental conditions that effect their performance, and which may even result in their failure. The current research proposes a mathematical model-based system for hydrostatic bearings that helps to improve its static/dynamic characteristics under varying conditions of performance-influencing variables such as temperature, spindle speed, external load, and clearance gap. To achieve these objectives, the capillary restrictors are replaced with servo valves, and a mathematical model is developed along with robust control design systems. The control system consists of feedforward and feedback control techniques that have not been applied before for hydrostatic bearings in the published literature. The feedforward control tries to remove a disturbance before it enters the system while feedback control achieves the objective of disturbance rejection and improves steady-state characteristics. The feedforward control is a trajectory-based controller and the feedback controller is a sliding mode controller with a PID sliding surface. The particle swarm optimization algorithm is used to tune the 6-dimensional vector of the tuning parameters with multi-objective performance criteria. Numerical investigations have been carried out to check the performance of the proposed system under varying conditions of viscosity, clearance gap, external load and the spindle speed. The comparison of our results with the published literature shows the effectiveness of the proposed system.

Comparative study for deriving stagedischarge– sediment concentration relationships using soft computing techniques

2021 ◽  
Vol 2 (104) ◽  
pp. 57-76
Author(s):  
P. Sihag ◽  
M.R. Sadikhani ◽  
V. Vambol ◽  
S. Vambol ◽  
A.K. Prabhakar ◽  
...  
Keyword(s):  
Random Forest ◽  
Soft Computing ◽  
Sediment Load ◽  
Prediction Models ◽  
Absolute Error ◽  
Load Prediction ◽  
Statistical Parameters ◽  
Hydro Power ◽  
Input Combination ◽  
Soft Computing Techniques

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

Seismic Response of Embankment Dams Based on Recorded Strong-Motion Data in Japan

2019 ◽  
Vol 35 (2) ◽  
pp. 955-976 ◽  
Author(s):  
DongSoon Park ◽  
Tadahiro Kishida
Keyword(s):  
Time Series ◽  
Seismic Response ◽  
Prediction Models ◽  
Strong Motion ◽  
Dynamic Responses ◽  
Design Stage ◽  
Ground Acceleration ◽  
Motion Data ◽  
Embankment Dams ◽  
Strong Motion Database

It is important to investigate strong-motion time series recorded at dams to understand their complex seismic responses. This paper develops a strong-motion database recorded at existing embankment dams and analyzes correlations between dam dynamic responses and ground-motion parameters. The Japan Commission on Large Dams database used here includes 190 recordings at the crests and foundations of 60 dams during 54 earthquakes from 1978 to 2012. Seismic amplifications and fundamental periods from recorded time series were computed and examined by correlation with shaking intensities and dam geometries. The peak ground acceleration (PGA) at the dam crest increases as the PGA at the foundation bedrock increases, but their ratio gradually decreases. The fundamental period broadly increases with the dam height and PGA at the foundation bedrock. The nonlinear dam response becomes more apparent as the PGA at the foundation bedrock becomes >0.2 g. The prediction models of these correlations are proposed for estimating the seismic response of embankment dams, which can inform the preliminary design stage.

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