scholarly journals The Search of Optimal Operating Regimes by Studying Velocity Fields in an Air Model of a Microhydroturbine

2020 ◽  
Vol 15 (2) ◽  
pp. 73-83
Author(s):  
Daniil A. Suslov ◽  
Ivan V. Litvinov ◽  
Yevgeny Yu. Gorelikov ◽  
Sergey I. Shtork
Keyword(s):  
Water Resource ◽  
Hydraulic Turbine ◽  
Optimal Operation ◽  
Velocity Fields ◽  
Optimal Regime ◽  
Optimal Operating ◽  
The Given ◽  
Operating Regimes ◽  
Air Medium ◽  
Swirl Parameter

This article is devoted to the search for conditions for optimal operation of a microhydroturbine model. The experiments were carried out in air medium. Velocity fields were measured in the outlet cone of a hydraulic turbine using an LDA system. It was shown that by modeling the flow in the air, using the integral swirl parameter S, it is possible to quickly determine the optimal regime of operation of the turbine for the given parameters of the water resource.

scholarly journals Use of Pareto Principle in Power System Mode Analysis

2010 ◽  
Vol 26 (1) ◽  
pp. 54-57
Author(s):  
Anatoly Mahnitko ◽  
Alexander Gavrilov
Keyword(s):  
Power System ◽  
System Model ◽  
Mode Analysis ◽  
Pareto Principle ◽  
Optimal Regime ◽  
Optimal Power ◽  
United Power System ◽  
System Mode ◽  
The Mathematical Model ◽  
The Given

Use of Pareto Principle in Power System Mode AnalysisThe optimal power dispatch problem in the power system is looked out in the given work. The mathematical model of power system optimal regime searching approach in the market conditions in accordance with Pareto principle is described. The theoretical layout is illustrated on a real power system model of the united power system, which consists of 17 nodes and 21 lines. The procedure is realized using the GAMS software.

The Reactor Size Influence on the Optimal Operating Setpoint Choice for a Fixed-Bed Multi-Tubular Catalytic Reactor

2018 ◽  
Vol 69 (8) ◽  
pp. 2012-2018
Author(s):  
Constantin Muscalu ◽  
Gheorghe Maria ◽  
Daniel Dinculescu
Keyword(s):  
Design Variable ◽  
Thermal Sensitivity ◽  
Fixed Bed ◽  
Heat Removal ◽  
Optimal Operation ◽  
Pareto Optimal ◽  
Optimal Operating ◽  
Control Variables ◽  
Reaction Heat ◽  
Pareto Optimal Front

Optimal operation of chemical reactors of high thermal sensitivity is a central engineering problem of very high current interest. One elegant alternative to choose the optimal setpoint when at least two contrary (opposite) objectives are considered is based on the so-called Pareto-optimal front technique. This paper exemplifies how to generate Pareto optimal operating policies when reactor productivity and safety objectives (expressed in probabilistic terms) are simultaneously considered in the presence of technological constraints, uncertainty in safety boundaries, and random fluctuations in control variables. Beside the operating control variables, one important design variable is the reactor pipe diameter because it is directly related to the reaction heat removal. This paper exemplifies the influence of this design variable on the setpoint choice when applying the Pareto-optimal front method with computing the runaway-boundaries by using the generalized sensitivity criterion of Morbidelli and Varma (MV-criterion). An example is provided for an industrial fixed-bed tubular reactor used for the catalytic oxidation of benzene to maleic anhydride (MA) in vapour phase.

Derivation of Monthly Operating Rules for Cascade Hydropower Stations Based on SVM

2013 ◽  
Vol 838-841 ◽  
pp. 1673-1676
Author(s):  
Ting Zhou ◽  
Chang Ming Ji ◽  
Bi Kui Zhao
Keyword(s):  
River System ◽  
Optimization Method ◽  
Optimal Operation ◽  
Support Vector ◽  
System Operation ◽  
Optimal Operating ◽  
Operating Rules ◽  
Hydropower Stations ◽  
System Power

In order to promote the efficiency of actual hydropower system operation under limited inflow forecast, an Implicit Stochastic Optimization method using Support Vector Machine (SVM) theory is proposed in this paper to derive long-term optimal operating rules. By applying the model to the Jinsha-Yangtze river system which is the largest hydropower base in China, fitting performance of operating rules is explained and evaluated. System simulation results are given and compared to deterministic optimal operation. Power output processes comparison shows that the average annual system power generation in two scenarios are 395TWh and 392TWh, and the overall operation processes are in well accordance with explicable inconsistency, which proves the efficiency of SVM in operating rules derivation for hydropower stations.

scholarly journals Investigation of suspended nanoliquid flow of Eyring–Powell fluid with gyrotactic microorganisms and density number

2020 ◽  
Vol 12 (9) ◽  
pp. 168781402092489
Author(s):  
Fazal Haq ◽  
Muhammad Ijaz Khan ◽  
Sohail A Khan ◽  
T Hayat
Keyword(s):  
Transfer Rate ◽  
Mass Transfer Rate ◽  
Skin Friction Coefficient ◽  
Velocity Fields ◽  
Gyrotactic Microorganisms ◽  
First Law Of Thermodynamics ◽  
Fluid Parameter ◽  
Curvature Parameter ◽  
Temperature And Velocity Fields ◽  
The Given

The aim of the current investigation is to discuss the behavior of mixed convection magnetohydrodynamic flow of Eyring–Powell nanoliquid subjected to gyrotactic microorganisms over a stretchable cylinder. Energy communication is developed through the first law of thermodynamics and deliberated in the manifestation of viscous dissipation. Furthermore, Brownian motion and thermophoresis effects are also considered. Nonlinear system of partial differential equations is altered into ordinary one due to employing transformations. The given systems are then solved through ND-solve technique. Impact of influential variables on velocity, motile microorganism’s temperature, and concentration is deliberated graphically. Skin friction coefficient, mass transfer rate, density number, and Nusselt number are numerically computed versus different influential variables. Velocity and temperature have opposite impact for curvature parameter. For higher estimation of fluid parameter, temperature and velocity fields boost up.

scholarly journals Mathematical Analysis of Optimal Operating Conditions in Heating Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Chan Kong ◽  
Yong Sun ◽  
Hongxi Zhang ◽  
Yongjiang Shi
Keyword(s):  
Water Temperature ◽  
Flow Rate ◽  
Heating System ◽  
Optimal Operation ◽  
Operating Conditions ◽  
Experimental Results ◽  
Optimal Operating ◽  
Heating Systems ◽  
Operation Conditions ◽  
Supply Water

With changes in the outdoor air temperature, the heat consumption of buildings also changes. Timely adjustment of the heating systems to ensure optimal operating conditions is extremely significant to save energy. In this study, the operation conditions of a heating system were analyzed numerically, and the existence, uniqueness, and stability of the optimal operation conditions of the heating system were proved. An operation optimization model that could obtain the optimal operation conditions was also established, and the correctness of the model was verified experimentally. Experimental results showed that when the flow rate was 0.606 m3/h, the supply water temperature was 67.13°C, water return temperature was 65.90°C, and the pump consumed the least amount of electricity. The experimental results and model calculation results showed that the operating cost is lower when the system flow rate is low and the supply water temperature is high under the same heat dissipation and indoor temperature.

Application of ANN Trained with GA in Energy Characteristics of Hydraulic Turbine

2010 ◽  
Vol 108-111 ◽  
pp. 692-695
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao ◽  
Chuan Hong Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Hydraulic Turbine ◽  
Optimal Operation ◽  
Practical Significance ◽  
Hydraulic Unit ◽  
Energy Characteristics ◽  
Artificial Neural Network Ann ◽  
The Relationship

The learning algorithm of artificial neural network (ANN) trained with genetic algorithm (GA) are introduced, based on the operation data of hydropower station, the network model of energy characteristics is established based on GA-ANN, the relationship curve between head H and output N is gained under some efficiency. The results show that the algorithm is better than BP neural network and avoid the limitations of BP neural network, the results can be used in the optimal operation of hydropower, and it has a practical significance. The results show the new model has a great importance in hydraulic unit study. It could be generalized into other all efficiency prediction, and it offers a new way in water conservancy and at the meantime a new method for the study of ANN and GA.

scholarly journals An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning

2020 ◽  
Vol 81 (8) ◽  
pp. 1578-1587 ◽  
Author(s):  
KiJeon Nam ◽  
SungKu Heo ◽  
Jorge Loy-Benitez ◽  
Pouya Ifaei ◽  
ChangKyoo Yoo
Keyword(s):  
Reinforcement Learning ◽  
Membrane Bioreactor ◽  
Optimal Operation ◽  
Treatment Efficiency ◽  
Optimal Operating ◽  
Operational Costs ◽  
Aeration System ◽  
Effluent Discharge ◽  
Aeration Process ◽  
Micro Organisms

Abstract Optimal operation of membrane bioreactor (MBR) plants is crucial to save operational costs while satisfying legal effluent discharge requirements. The aeration process of MBR plants tends to use excessive energy for supplying air to micro-organisms. In the present study, a novel optimal aeration system is proposed for dynamic and robust optimization. Accordingly, a deep reinforcement learning (DRL)-based optimal operating system is proposed, so as to meet stringent discharge qualities while maximizing the system's energy efficiency. Additionally, it is compared with the manual system and conventional reinforcement learning (RL)-based systems. A deep Q-network (DQN) algorithm automatically learns how to operate the plant efficiently by finding an optimal trajectory to reduce the aeration energy without degrading the treated water quality. A full-scale MBR plant with the DQN-based autonomous aeration system can decrease the MBR's aeration energy consumption by 34% compared to other aeration systems while maintaining the treatment efficiency within effluent discharge limits.

scholarly journals Choosing the optimal operating mode of a gas condensate well taking into account the relaxation deformation of rocks

2021 ◽  
Vol 134 (3) ◽  
pp. 50-54
Author(s):  
Т. А. Samadov ◽  
◽  
B. Z. , Кazymov ◽  
S. H. Novruzova ◽  
◽  
...  
Keyword(s):  
Operation Mode ◽  
Operating Mode ◽  
Optimal Operation ◽  
Gas Condensate ◽  
Reservoir Pressure ◽  
Optimal Operating ◽  
Reservoir Rocks ◽  
Bottom Hole ◽  
Sand Accumulation ◽  
Current Operating

The article proposes a method for selecting the optimal operation mode of a gas condensate well without sand accumulation in the bottom, taking into account the relaxation deformation of reservoir rocks during the development of a gas condensate deposit in the depletion mode. This method simultaneously allows you to determine the required current operating volume of produced condensate (as well as gas), as well as bottom-hole and contour values of reservoir pressure, condensate saturation and porosity of the reservoir, corresponding to the selected optimal operation mode of the well.

Extremum Control for Optimal Operating Point Determination and Set Point Optimization Via Sliding Modes

2000 ◽  
Vol 122 (4) ◽  
pp. 719-724 ◽  
Author(s):  
I˙brahim Haskara ◽  
U¨mit O¨zgu¨ner ◽  
Jim Winkelman
Keyword(s):  
Time Scale ◽  
Closed Loop ◽  
Sliding Mode ◽  
Operation Mode ◽  
Research Problem ◽  
Optimization Method ◽  
Optimal Operation ◽  
Operating Point ◽  
Limited Information ◽  
Optimal Operating

In the design of a control system, it is often desirable to operate at the peak of an appropriate performance surface which characterizes the performance of the closed-loop operation. However, in many cases, only limited information might be available on the plant and the desired performance criterion which makes a priori determination of such an optimal operation mode difficult in the first place. The online identification of an optimal operating point and the development of a controller structure which enables the system to robustly operate at such a point constitute a remarkable research problem with this motivation. In this paper, a two-time scale sliding mode optimization method is studied for this purpose. The adopted scheme assumes a regulative controller which produces an equilibria for the closed-loop system parametrized by a free control parameter and employs a sliding mode optimization method to adapt this parameter in a slower time scale to increase the performance of the overall system. A simulation study is summarized to illustrate the approach. [S0022-0434(00)01004-2]

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