An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks

2020 ◽  
Vol 274 ◽  
pp. 115322
Author(s):  
Bing Su ◽  
Shengwei Wang
Keyword(s):  
Optimal Control ◽  
Sensor Networks ◽  
Real Time ◽  
Control Strategy ◽  
Hvac Systems ◽  
Time Optimal Control ◽  
Smart Sensor ◽  
Optimal Control Strategy ◽  
Agent Based ◽  
Time Optimal

Real-Time Optimal Power Management for Hybrid Electric Vehicle Based on Prediction of Trip Information

2013 ◽  
Vol 321-324 ◽  
pp. 1539-1547 ◽  
Author(s):  
Li Cun Fang ◽  
Gang Xu ◽  
Tian Li Li ◽  
Ke Min Zhu
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Power Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Moving Window ◽  
Optimal Control Strategy ◽  
Time Optimal

Power management of hybrid electric vehicle (HEV) is an important operational factor for HEV to enhance fuel economy and reduce emissions. Optimal control for HEV requires the knowledge of entire driving cycle and elevation profile to obtain the optimal control strategy over fixed driving cycle. In this paper, the traffic knowledge extracted from intelligent transportation systems (ITSs),global positioning systems (GPSs) and geographical information systems (GISs) is used for predicting the knowledge of the future driving cycle, and the real-time optimal control strategy based on dynamic programming in a moving window is investigated in order to minimize fuel consumption. A simulation study was conducted for two driving cycles, and the results showed significant improvement in fuel economy compared with a rule-based control. Furthermore, the results showed that the distance of the moving window has obvious effect on the fuel economy.

Discrete Adjoint Sensitivities for the Real-Time Optimal Control of Large District Heating Networks During Failure Events

2016 ◽  
Author(s):  
Alberto Pizzolato ◽  
Adriano Sciacovelli ◽  
Vittorio Verda
Keyword(s):  
Optimal Control ◽  
Temperature Field ◽  
Real Time ◽  
Control Strategy ◽  
District Heating ◽  
Time Optimal Control ◽  
The Real ◽  
Discrete Adjoint ◽  
Time Optimal ◽  
Heating Networks

In this paper, we propose an innovative approach for the real-time optimal control of district heating networks during anomalous conditions. We aim at minimizing the maximum thermal discomfort of the connected users after a pipe breakage by an integrated and centralized management of the user control-valves. Our control strategy uses a gradient-based optimizer driven by discrete adjoint sensitivities, which makes it fast and nearly insensitive to the problem dimensions. We tested the proposed approach by simulating a set of different malfunctions in the Turin District heating network and by analyzing the building temperature field during the optimizer convergence history. Compared to the control strategy in use today, we observe that our approach flattens the temperature field and eliminates discomfort peaks, bringing a considerable increase of the minimum user temperature which ranges from a minimum of 1.8 °C to a maximum of 15.4 °C. Furthermore, our optimization strategy allows for superior results to what is achievable conventionally with an 85 % increase of the pumping head, making back-up pumping devices a non-necessary investment.

Observer-based time-optimal control of an aerobic SBR for chemical and petrochemical wastewater treatment

2000 ◽  
Vol 42 (5-6) ◽  
pp. 163-170 ◽  
Author(s):  
A. Vargas ◽  
G. Soto ◽  
J. Moreno ◽  
G. Buitrón
Keyword(s):  
Optimal Control ◽  
Reaction Time ◽  
Control Strategy ◽  
Optimal Operation ◽  
Time Optimal Control ◽  
Nonlinear Observer ◽  
Synthetic Wastewater ◽  
Optimal Control Strategy ◽  
Time Optimal ◽  
High Concentrations

The present study implements a time-optimal control strategy for a discontinuous aerobic bioreactor, used to treat highly concentrated toxic wastewater present in some effluents of the chemical and petrochemical industries, using respirometric techniques. The control strategy regulates the feed rate to maintain a constant optimal substrate concentration in the reactor, which in turn minimizes the reaction time. Since this control requires on-line knowledge of unmeasurable variables, an Extended Kalman Filter is used as a nonlinear observer. The experimental setup was a 7 litre laboratory bioreactor used to treat synthetic wastewater with high concentrations of 4-chlorophenol. The controller consisted of a personal computer with data acquisition hardware and real-time software tools, peristaltic pumps and an electronic oxygen meter. Three experiments were performed: one to obtain parameters and calibrate the observer, another one to validate the time-optimal strategy and a final one to evaluate theperformance of a fully automated time-optimal operation. When well calibrated, the observer provided good enough estimates and the controller worked as expected, reducing reaction time and increasing the overall efficiency of the bioreactor, when compared with the usual SBR-type operation.

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