Deep deterministic policy gradient-based combustion optimization method for coal-fired boiler

2021 ◽  
Author(s):  
Xuqi Bao ◽  
Yiguo Li ◽  
Chuanpeng Zhu
Keyword(s):  
Optimization Method ◽  
Policy Gradient ◽  
Gradient Based ◽  
Combustion Optimization

scholarly journals Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1036 ◽  
Author(s):  
Xinying Xu ◽  
Qi Chen ◽  
Mifeng Ren ◽  
Lan Cheng ◽  
Jun Xie
Keyword(s):  
Power Plant ◽  
Combustion Process ◽  
Optimization Method ◽  
Combustion Efficiency ◽  
Pollutant Emissions ◽  
Combustion Model ◽  
Nox Emissions ◽  
Coupling Characteristics ◽  
Learning Machine ◽  
Combustion Optimization

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.

scholarly journals Deep Deterministic Policy Gradient Based on Double Network Prioritized Experience Replay

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chaohai Kang ◽  
Chuiting Rong ◽  
Weijian Ren ◽  
Fengcai Huo ◽  
Pengyun Liu
Keyword(s):  
Double Network ◽  
Policy Gradient ◽  
Experience Replay ◽  
Gradient Based

An Improved Shape Annealing Method for Truss Topology Generation

1994 ◽  
Author(s):  
Giridhar Reddy ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Optimization Method ◽  
Geometric Constraints ◽  
Truss Structures ◽  
Model Design ◽  
Topology Generation ◽  
Truss Topology ◽  
Gradient Based ◽  
Annealing Algorithm ◽  
Annealing Method

Abstract A method for the design of truss structures which encourages lateral exploration, pushes away from violated spaces, models design intentions, and produces solutions with a wide variety of characteristics is introduced. An improved shape annealing algorithm for truss topology generation and optimization, based on the techniques of shape grammars and simulated annealing, implements the method. The algorithm features a shape grammar to model design intentions, an ability to incorporate geometric constraints to avoid obstacles, and a shape optimization method using only simulated annealing with more consistent convergence characteristics; no traditional gradient-based techniques are employed. The improved algorithm is illustrated on various structural examples generating a variety of solutions based on a simple grammar.

scholarly journals Inverse design of grating couplers using the policy gradient method from reinforcement learning

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sean Hooten ◽  
Raymond G. Beausoleil ◽  
Thomas Van Vaerenbergh
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Method ◽  
Photonic Devices ◽  
Inverse Design ◽  
Grating Couplers ◽  
Electromagnetic Devices ◽  
Policy Gradient ◽  
Gradient Based ◽  
Local Gradient

Abstract We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic Optimization using REINFORCE Criteria for Enhanced Design). This technique uses a probabilistic generative neural network interfaced with an electromagnetic solver to assist in the design of photonic devices, such as grating couplers. We show that PHORCED obtains better performing grating coupler designs than local gradient-based inverse design via the adjoint method, while potentially providing faster convergence over competing state-of-the-art generative methods. As a further example of the benefits of this method, we implement transfer learning with PHORCED, demonstrating that a neural network trained to optimize 8° grating couplers can then be re-trained on grating couplers with alternate scattering angles while requiring >10× fewer simulations than control cases.

Policy Gradient-Based Core Placement Optimization for Multichip Many-Core Systems

2021 ◽  
pp. 1-15
Author(s):  
Wooshik Myung ◽  
Donghyun Lee ◽  
Chenhang Song ◽  
Guanrui Wang ◽  
Cheng Ma
Keyword(s):  
Placement Optimization ◽  
Policy Gradient ◽  
Gradient Based ◽  
Many Core

Efficient Structural Design Using a Numerical Optimization Technique

2019 ◽  
Author(s):  
Qian Wang ◽  
Lucas Schmotzer ◽  
Yongwook Kim
Keyword(s):  
Structural Design ◽  
Numerical Optimization ◽  
Optimization Technique ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Convergence Criteria ◽  
Efficient Design ◽  
Concrete Building ◽  
Gradient Based ◽  
Structural Responses

<p>Structural designs of complex buildings and infrastructures have long been based on engineering experience and a trial-and-error approach. The structural performance is checked each time when a design is determined. An alternative strategy based on numerical optimization techniques can provide engineers an effective and efficient design approach. To achieve an optimal design, a finite element (FE) program is employed to calculate structural responses including forces and deformations. A gradient-based or gradient-free optimization method can be integrated with the FE program to guide the design iterations, until certain convergence criteria are met. Due to the iterative nature of the numerical optimization, a user programming is required to repeatedly access and modify input data and to collect output data of the FE program. In this study, an approximation method was developed so that the structural responses could be expressed as approximate functions, and that the accuracy of the functions could be adaptively improved. In the method, the FE program was not required to be directly looped in the optimization iterations. As a practical illustrative example, a 3D reinforced concrete building structure was optimized. The proposed method worked very well and optimal designs were found to reduce the torsional responses of the building.</p>

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