scholarly journals Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Intissar Khoja ◽  
Taoufik Ladhari ◽  
Anis Sakly ◽  
Faouzi M’sahli
Keyword(s):  
Wastewater Treatment ◽  
Particle Swarm Optimization ◽  
Activated Sludge ◽  
Parameter Identification ◽  
Treatment Process ◽  
Classical Method ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Wastewater Treatment Process ◽  
Swarm Optimization

The current paper is entirely devoted to show the applicability of Particle Swarm Optimization (PSO) algorithm as a parameter identification method for a representative model of an Activated Sludge Wastewater Treatment Process (ASWWTP) with alternating phases. The model of identification is composed of two linear submodels: one for the aerobic phase and the other for the anoxic phase. In order to prove the efficiency of the proposed method, its performance is compared with another classical method called Simplex Search Algorithm (SSA) as well as with the experimental data.

scholarly journals Reinforcement learning-based particle swarm optimization for sewage treatment control

2021 ◽  
Author(s):  
Lu Lu ◽  
Hui Zheng ◽  
Jing Jie ◽  
Miao Zhang ◽  
Rui Dai
Keyword(s):  
Wastewater Treatment ◽  
Particle Swarm Optimization ◽  
Reinforcement Learning ◽  
Energy Consumption ◽  
Learning Strategy ◽  
Sewage Treatment ◽  
Particle Swarm ◽  
High Energy ◽  
Wastewater Treatment Process ◽  
Swarm Optimization

AbstractTo solve the problem of high-energy consumption in activated sludge wastewater treatment, a reinforcement learning-based particle swarm optimization (RLPSO) was proposed to optimize the control setting in the sewage process. This algorithm tries to take advantage of the valid history information to guide the behavior of particles through a reinforcement learning strategy. First, an elite network is constructed by selecting elite particles and recording their successful search behavior. Then the network is trained and evaluated to effectively predict the particle velocity. In the periodic wastewater treatment process, the RLPSO runs repeatedly according to the optimized cycle. Finally, RLPSO was tested based on Benchmark Simulation Model 1 (BSM1) of sewage treatment, and the simulation results showed that it could effectively reduce the energy consumption on the premise of ensuring qualified water quality. Furthermore, the performance of RLPSO was analyzed using the benchmarks with higher dimension, which verifies the effectiveness of the algorithm and provides the possibility for RLPSO to be applied to a wider range of problems.

scholarly journals Parameter Identification of Anaerobic Wastewater Treatment Bioprocesses Using Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Dorin Sendrescu
Keyword(s):  
Wastewater Treatment ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Wastewater Treatment Process ◽  
Swarm Optimization ◽  
Anaerobic Wastewater Treatment ◽  
Complex Optimization

This paper deals with the offline parameters identification for a class of wastewater treatment bioprocesses using particle swarm optimization (PSO) techniques. Particle swarm optimization is a relatively new heuristic method that has produced promising results for solving complex optimization problems. In this paper one uses some variants of the PSO algorithm for parameter estimation of an anaerobic wastewater treatment process that is a complex biotechnological system. The identification scheme is based on a multimodal numerical optimization problem with high dimension. The performances of the method are analyzed by numerical simulations.

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

scholarly journals Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 839
Author(s):  
Shaofei Sun ◽  
Hongxin Zhang ◽  
Xiaotong Cui ◽  
Liang Dong ◽  
Muhammad Saad Khan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication Systems ◽  
Optimization Problems ◽  
Classical Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electromagnetic Analysis ◽  
Swarm Optimization ◽  
Test Board

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.

scholarly journals Unravelling diversity and metabolic potential of microbial consortia at each stage of leather sewage treatment

RSC Advances ◽  
2017 ◽  
Vol 7 (66) ◽  
pp. 41727-41737 ◽  
Author(s):  
Hebin Liang ◽  
Dongdong Ye ◽  
Lixin Luo
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Microbial Consortia ◽  
Biological Wastewater Treatment ◽  
Metabolic Potential ◽  
Ecological Functions ◽  
Wastewater Treatment Process ◽  
System P

Activated sludge is essential for the biological wastewater treatment process and the identification of active microbes enlarges awareness of their ecological functions in this system.

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