The effect of extrusion operating conditions on the on‐line apparent viscosity of 98% Amylopectin (Amioca) and 70% Amylose (Hylon 7) corn starches during extrusion

1990 ◽  
Vol 34 (8) ◽  
pp. 1245-1266 ◽  
Author(s):  
L. S. Lai ◽  
J. L. Kokini
Keyword(s):  
Apparent Viscosity ◽  
Operating Conditions ◽  
On Line

Validation of On-line Respiration Meter and its Applications by Computer Simulation

1992 ◽  
Vol 26 (5-6) ◽  
pp. 1355-1363 ◽  
Author(s):  
C-W. Kim ◽  
H. Spanjers ◽  
A. Klapwijk
Keyword(s):  
Steady State ◽  
Activated Sludge ◽  
Respiration Rate ◽  
Oxygen Demand ◽  
Operating Conditions ◽  
Mass Balances ◽  
Short Term ◽  
Respiration Rates ◽  
On Line ◽  
Made In

An on-line respiration meter is presented to monitor three types of respiration rates of activated sludge and to calculate effluent and influent short term biochemical oxygen demand (BODst) in the continuous activated sludge process. This work is to verify if the calculated BODst is reliable and the assumptions made in the course of developing the proposed procedure were acceptable. A mathematical model and a dynamic simulation program are written for an activated sludge model plant along with the respiration meter based on mass balances of BODst and DO. The simulation results show that the three types of respiration rate reach steady state within 15 minutes under reasonable operating conditions. As long as the respiration rate reaches steady state the proposed procedure calculates the respiration rate that is equal to the simulated. Under constant and dynamic BODst loading, the proposed procedure is capable of calculating the effluent and influent BODst with reasonable accuracy.

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

scholarly journals The Effect of the Operating Conditions on the Apparent Viscosity of Crude Palm Oil During Oil Clarification

1970 ◽  
Vol 5 (1) ◽  
Author(s):  
Sulaiman Al-Zuhair, Mirghani I. Ahmed and Yousif A. Abakr
Keyword(s):  
Shear Rate ◽  
Boundary Conditions ◽  
Palm Oil ◽  
Apparent Viscosity ◽  
Operating Conditions ◽  
Effect Of Temperature ◽  
Optimum Operating ◽  
Crude Palm Oil ◽  
Different Boundary Conditions ◽  
Rotary Viscometer

This paper discusses the apparent viscosity of crude palm oil, using rotary viscometer, under different boundary conditions. It was experimentally shown that the apparent viscosity of palm oil drops with increasing of the shear rate and the temperature.  However, the effect of temperature on the viscosity tends to fade at temperatures beyond 80 oC.  A correlation between the apparent viscosity of crude palm oil and the operating conditions was developed. This correlation can be used in design of crude palm oil settlers and in determining the optimum operating conditions.Key Words:  Crude palm oil, apparent viscosity, shear rate, modelling, separation 

Intelligent Adaptive Active Force Control of a Robotic Arm with Embedded Iterative Learning Algorithms

2012 ◽  
Author(s):  
Musa Mailah ◽  
Miaw Yong Ong
Keyword(s):  
Force Control ◽  
Learning Algorithms ◽  
Iterative Learning ◽  
Operating Conditions ◽  
Robotic Arm ◽  
Active Force ◽  
Inertia Matrix ◽  
Active Force Control ◽  
Controller Gain ◽  
On Line

Kawalan jitu dan lasak bagi satu sistem lengan robot atau pengolah adalah amat penting terutama sekali jika sistem mengalami pelbagai bentuk bebanan dan keadaan pengendalian. Kertas kerja ini memaparkan satu kaedah baru dan lasak untuk mengawal lengan robot menggunakan teknik pembelajaran secara berlelaran yang dimuatkan dalam strategi kawalan daya aktif. Sebanyak dua algoritma pembelajaran utama digunakan dalam kajian – yang pertama digunakan untuk menala gandaan pengawal secara automatik manakala yang satu lagi pula untuk menganggarkan matriks inersia pengolah. Kedua-dua parameter ini dihasilkan secara adaptif dan dalam talian ketika robot sedang menjalankan tugas menjejak trajektori dalam persekitaran tindakan daya gangguan. Dalam kajian ini, pengetahuan awal tentang kedua–dua nilai gandaan pengawal dan anggaran matriks inersia tidak wujud. Dengan demikian, suatu skema kawalan yang jitu dan lasak terhasil. Keberkesanan kaedah yang dicadangkan dapat ditentusahkan melalui hasil kajian yang diperoleh dan dibentangkan dalam kertas kerja ini. Kata kunci: Adaptif; kawalan daya aktif; pembelajaran berlelaran; matriks inersia; gandaan pengawal The robust and accurate control of a robotic arm or manipulator are of prime importance especially if the system is subjected to varying forms of loading and operating conditions. The paper highlights a novel and robust method to control a robotic arm using an iterative learning technique embedded in an active force control strategy. Two main iterative learning algorithms are utilized in the study – the first is used to automatically tune the controller gains while the second to estimate the inertia matrix of the manipulator. These parameters are adaptively computed on-line while the robot is executing a trajectory tracking task and subject to some forms of external disturbances. No priori knowledge of both the controller gains and the estimated inertia matrix are ever assumed in the study. In this way, an adaptive and robust control scheme is derived. The effectiveness of the method is verified and can be seen from the results of the work presented in this paper. Keywords: Adaptive; active force control; iterative learning; inertia matrix; controller gain

Effects of the Infusion Mode on Bicarbonate Balance in On-Line Hemodiafiltration

2002 ◽  
Vol 25 (2) ◽  
pp. 100-106 ◽  
Author(s):  
L.A. Pedrini ◽  
V. De Cristofaro ◽  
B. Pagliari
Keyword(s):  
Driving Force ◽  
Operating Conditions ◽  
Bicarbonate Transport ◽  
Dialysis Patients ◽  
Acid Base Balance ◽  
Bicarbonate Concentration ◽  
Acid Base ◽  
Base Balance ◽  
On Line ◽  
Intermediate Results

Background Electrolyte and acid-base balance may be differently affected by the infusion mode in on-line hemodiafiltration (HDF). We studied the effects of the different infusion modes on bicarbonate transport across the dialyzer membrane, and thus on the final bicarbonate balance of the HDF sessions. Methods Instantaneous HCO3− transfer across the dialyzer membrane, blood bicarbonate profile and the total balance of the sessions were studied in six dialysis patients under the same operating conditions over 36 HDF sessions, in order to compare the effects of predilution HDF (pre-HDF), postdilution HDF (post-HDF), and mixed HDF on the final bicarbonate balance. Results The final HCO3− balance was more positive in post-HDF vs pre-HDF (142 ± 36 vs 99 ± 41 mmol/session, p<0.05), with a final blood HCO3− concentration of 26.6 ± 1.0 vs 25.4 ± 1.1 mmol/L, (p<0.05). Mixed HDF yielded intermediate results (balance: 119 ± 42 mmol/session, final HCO3− 26.2 (1.2 mmol/L). These differences were seen to result from the increased HCO3- concentration of blood entering the filter in predilution, due to the infused HCO3−, enhancing convective loss and reducing the driving force for diffusive HCO3− gain. Conclusions Bicarbonate concentration in dialysate-reinfusate is critical in order to obtain an adequate end of session HCO3− balance in on-line HDF. The predilution method produced the lowest cumulative net HCO3− gain between the three studied infusion modes. Our data suggest that, under the same operating conditions and excluding the effect of ultrafiltration, dialysate HCO3− should be increased by about 2 mmol/L in pre-HDF, and 1 mmol/L in mixed HDF, to yield the same final balance as in post-HDF.

An Intelligent Sensor Validation and Fault Diagnostic Technique for Diesel Engines

1999 ◽  
Vol 123 (1) ◽  
pp. 141-144 ◽  
Author(s):  
Ehsan Mesbahi
Keyword(s):  
Fault Diagnosis ◽  
Confidence Level ◽  
Diagnostic Technique ◽  
Operating Conditions ◽  
Sensor Reading ◽  
Intelligent Sensor ◽  
Sensor Validation ◽  
Wide Range ◽  
On Line ◽  
Artificial Neural Network Ann

Abstract An intelligent sensor validation and on-line fault diagnosis technique for a 6 cylinder turbocharged diesel engine is proposed and studied. A single auto-associative 3-layer Artificial Neural Network (ANN), is trained to examine the accuracy of the measured data and allocate a confidence level to each signal. The same ANN is used to recover the missing or faulty data with a close approximation. For on-line fault detection a feed-forward ANN is trained to classify and consequently recognize faulty and healthy behavior of the engine for a wide range of operating conditions. The proposed technique is also equipped with an on-line learning mechanism, which is activated when the confidence level in predicted fault is poor. It is hoped that a feasible, practical, and reliable sensor reading, as well as highly accurate fault diagnosis system, would be achieved.

Indirect Neural Network Adaptive Robust Control of Linear Motor Drive System

2002 ◽  
Author(s):  
J. Q. Gong ◽  
Bin Yao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Tracking Error ◽  
Linear Motor ◽  
Adaptive Robust Control ◽  
Operating Conditions ◽  
Tracking Performance ◽  
Motor Drive ◽  
Output Tracking ◽  
On Line

In this paper, an indirect neural network adaptive robust control (INNARC) scheme is developed for the precision motion control of linear motor drive systems. The proposed INNARC achieves not only good output tracking performance but also excellent identifications of unknown nonlinear forces in system for secondary purposes such as prognostics and machine health monitoring. Such dual objectives are accomplished through the complete separation of unknown nonlinearity estimation via neural networks and the design of baseline adaptive robust control (ARC) law for output tracking performance. Specifically, recurrent neural network (NN) structure with NN weights tuned on-line is employed to approximate various unknown nonlinear forces of the system having unknown forms to adapt to various operating conditions. The design is actual system dynamics based, which makes the resulting on-line weight tuning law much more robust and accurate than those in the tracking error dynamics based direct NNARC designs in implementation. With a controlled learning process achieved through projection type weights adaptation laws, certain robust control terms are constructed to attenuate the effect of possibly large transient modelling error for a theoretically guaranteed robust output tracking performance in general. Experimental results are obtained to verify the effectiveness of the proposed INNARC strategy. For example, for a typical point-to-point movement, with a measurement resolution level of ±1μm, the output tracking error during the entire execution period is within ±5μm and mainly stays within ±2μm showing excellent output tracking performance. At the same time, the outputs of NNs approximate the unknown forces very well allowing the estimates to be used for secondary purposes such as prognostics.

scholarly journals Improving the Performance of Process Controllers Using a New Clustered Neural Network

2011 ◽  
Vol 1 ◽  
pp. 273-277
Author(s):  
M. Reza Soleymani Yazdi ◽  
Michel Guillot
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Operating Conditions ◽  
Machining Processes ◽  
Workpiece Material ◽  
Control Scheme ◽  
Organization Capacity ◽  
First Results ◽  
On Line ◽  
Neuro Controller

This paper presents first a newly developed clustered neural network, which incorporates self-organization capacity into the well-known common multilayer perceptron (MLP) architecture. With this addition, it is possible to reduce significantly overall memory degradation of the neuro-controller during on-line training. In the second part of the paper, this clustered multilayer perceptron (CMLP) network is applied and compared to the MLP through modeling and simulations of machining processes. Simulation results presented using machining data demonstrate that the CMLP possesses more powerful modeling capacity than the standard MLP, offers better adaptability to new operating conditions, and finally performs more reliably. During on-line training with machining data about 65% degradation of previously learned information can be observed in the MLP as opposed to only 11% for the CMLP. Finally, an adaptive control scheme intended for on-line optimization of the machining processes is presented. This scheme uses a feed forward CMLP inverse neuro-controller which learns off-line and on-line the relationships between process inputs and output under simulated perturbations (i.e., tool wear and non-homogeneous workpiece material properties). The first results using the CMLP inverse neuro-controller are promising

scholarly journals Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

2020 ◽  
Vol 10 (21) ◽  
pp. 7836
Author(s):  
Cher Ming Tan ◽  
Preetpal Singh ◽  
Che Chen
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Estimation Method ◽  
Lithium Ion ◽  
Threshold Level ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
State Of Health ◽  
On Line ◽  
Useful Life

Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.

On-line determination of nitrite in wastewater treatment by use of a biosensor

2002 ◽  
Vol 45 (4-5) ◽  
pp. 69-76 ◽  
Author(s):  
M. Nielsen ◽  
N.P. Revsbech ◽  
L.H. Larsen ◽  
A. Lynggard-Jensen
Keyword(s):  
Activated Sludge ◽  
Mechanical Damage ◽  
Operating Conditions ◽  
Bacterial Populations ◽  
Operational Lifetime ◽  
On Line ◽  
Low Levels ◽  
In The Beginning

A newly developed biosensor for nitrite having a 90% response time of about 1 min was used to monitor nitrite concentration in activated sludge exposed to oxic/anoxic cycles. The NO2− biosensor contains bacteria that reduce NO2−, but not NO3−, to N2O that is subsequently monitored by a built-in electrochemical sensor. Nitrite plus nitrate (NOx−) was simultaneously monitored by a NOx− biosensor. The maximum operational lifetime of the NO2− biosensor was 6 weeks, but much longer lifetimes can be expected as malfunctioning by the 3 sensors used for longer periods was due to either mechanical damage or ineffective internal sterilization during the construction. Insufficiently sterilized sensors became sensitive also to NO3− after some time due to development of NO3−-reducing bacterial populations within the sensor. The fraction of NO2− as compared to NO3− in the activated sludge was very dependent on prehistory, actual loading, and aeration. During balanced operation with NH4+ being exhausted during the later parts of the aerobic cycle, NO2− increased in concentration up to about 50 μM during the early part of the aeration cycle until NH4+ became limiting. At that time the NO2− concentration decreased to low levels. Under some operating conditions a peak of NO2− also appeared in the beginning of the anoxic period. NO2− and NO3− were depleted simultaneously during the anoxic period.

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