scholarly journals Modelling and optimized water management of artificial inland waterway systems

2012 ◽  
Vol 15 (2) ◽  
pp. 348-365 ◽  
Author(s):  
J. Wagenpfeil ◽  
E. Arnold ◽  
H. Linke ◽  
O. Sawodny
Keyword(s):  
Water Management ◽  
State Estimation ◽  
Process Model ◽  
Water Levels ◽  
Estimation Methods ◽  
Strong Impact ◽  
Initial State ◽  
Optimal Control Strategy ◽  
Model Predictive Controller ◽  
Predictive Controller

A decision support system (DSS) for optimized operational water management of artificial inland waterways is presented. It will be deployed as part of a supervisory control and data acquisition (SCADA) system of the Mittellandkanal (MLK), a large canal structure in northern Germany, and relies on experience gained from a similar system. The DSS uses a model predictive controller with a 48 h prediction horizon to calculate optimal pump and discharge strategies that will ensure navigable water levels and at the same time minimize operational costs. The internal process model for the model predictive controller is obtained from a numerical integration of the Saint Venant equations using Godunov's method. The initial state needed for an accurate prediction is estimated using moving horizon state estimation (MHE) or unscented Kalman filtering. Additionally, the state estimation methods are used to estimate non-measurable disturbance inflows, which may have a strong impact on the control performance if not compensated for by the model predictive controller. The optimal control strategy is transformed into discrete-valued pump and discharge jobs that account for technical and operational input constraints. Closed-loop simulations with a high-resolution hydrodynamic numerical model of the MLK illustrate the ability of the control algorithm to adapt to model uncertainties and non-controllable inputs.

scholarly journals ANN Approach for State Estimation of Hybrid Systems and Its Experimental Validation

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Shijoh Vellayikot ◽  
M. V. Vaidyan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Hybrid Systems ◽  
Process Model ◽  
Unscented Kalman Filter ◽  
State Estimator ◽  
Initial State ◽  
Process Noise ◽  
State Estimators ◽  
Tank System

A novel artificial neural network based state estimator has been proposed to ensure the robustness in the state estimation of autonomous switching hybrid systems under various uncertainties. Taking the autonomous switching three-tank system as benchmark hybrid model working under various additive and multiplicative uncertainties such as process noise, measurement error, process–model parameter variation, initial state mismatch, and hand valve faults, real-time performance evaluation by the comparison of it with other state estimators such as extended Kalman filter and unscented Kalman Filter was carried out. The experimental results reported with the proposed approach show considerable improvement in the robustness in performance under the considered uncertainties.

Biorefinery implementation for recovery debottlenecking at existing pulp mills- Part II: Techno-economic evaluation

TAPPI Journal ◽  
2012 ◽  
Vol 11 (8) ◽  
pp. 17-24 ◽  
Author(s):  
HAKIM GHEZZAZ ◽  
LUC PELLETIER ◽  
PAUL R. STUART
Keyword(s):  
Cost Estimation ◽  
Process Model ◽  
Black Liquor ◽  
Pulp Mill ◽  
Value Added ◽  
Economic Assessment ◽  
Extraction Process ◽  
Estimation Methods ◽  
Precipitation Process

The evaluation and process risk assessment of (a) lignin precipitation from black liquor, and (b) the near-neutral hemicellulose pre-extraction for recovery boiler debottlenecking in an existing pulp mill is presented in Part I of this paper, which was published in the July 2012 issue of TAPPI Journal. In Part II, the economic assessment of the two biorefinery process options is presented and interpreted. A mill process model was developed using WinGEMS software and used for calculating the mass and energy balances. Investment costs, operating costs, and profitability of the two biorefinery options have been calculated using standard cost estimation methods. The results show that the two biorefinery options are profitable for the case study mill and effective at process debottlenecking. The after-tax internal rate of return (IRR) of the lignin precipitation process option was estimated to be 95%, while that of the hemicellulose pre-extraction process option was 28%. Sensitivity analysis showed that the after tax-IRR of the lignin precipitation process remains higher than that of the hemicellulose pre-extraction process option, for all changes in the selected sensitivity parameters. If we consider the after-tax IRR, as well as capital cost, as selection criteria, the results show that for the case study mill, the lignin precipitation process is more promising than the near-neutral hemicellulose pre-extraction process. However, the comparison between the two biorefinery options should include long-term evaluation criteria. The potential of high value-added products that could be produced from lignin in the case of the lignin precipitation process, or from ethanol and acetic acid in the case of the hemicellulose pre-extraction process, should also be considered in the selection of the most promising process option.

Enlarging the region of stability in robust model predictive controller based on dual-mode control

2021 ◽  
pp. 014233122110123
Author(s):  
Fatemeh Khani ◽  
Mohammad Haeri
Keyword(s):  
Stability Region ◽  
Linear Matrix ◽  
Input State ◽  
Dual Mode ◽  
Model Predictive Controller ◽  
Mode Control ◽  
Robust Model ◽  
Output Constraints ◽  
Predictive Controller ◽  
The Stability

Industrial processes are inherently nonlinear with input, state, and output constraints. A proper control system should handle these challenging control problems over a large operating region. The robust model predictive controller (RMPC) could be an linear matrix inequality (LMI)-based method that estimates stability region of the closed-loop system as an ellipsoid. This presentation, however, restricts confident application of the controller on systems with large operating regions. In this paper, a dual-mode control strategy is employed to enlarge the stability region in first place and then, trajectory reversing method (TRM) is employed to approximate the stability region more accurately. Finally, the effectiveness of the proposed scheme is illustrated on a continuous stirred tank reactor (CSTR) process.

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