Accelerating Genetic Algorithm Research Articles

Stochastic evolutionary-based optimization for rapid diagnosis and energy-saving in pilot- and full-scale Carrousel oxidation ditches

Energy consumption is a primary issue needed to be considered for wastewater treatment targeting qualified effluent. In this paper, a hybrid model is proposed for rapid diagnosis of operational conditions meeting requirements of discharge standards and energy saving in Carrousel Oxidation Ditches (ODs). Based on a three-dimensional (3D) three-phase computational fluid dynamics (CFD) model, we developed an artificial neural network (ANN) model with back propagation algorithm and an accelerating genetic algorithm (AGA) model to achieve real-time simulation and system optimization in the Carrousel ODs. By incorporating the 3D-CFD and multi-site ANN models, the hybrid model provided reasonable predictions of liquid flow, sludge sedimentation and water quality in the Carrousel ODs. With help of the AGA model based on evolution theory, system optimization could be reached to meet multiple purposes such as energy saving, water-quality improving and normal sludge distribution, which was successfully demonstrated in both pilot- and full-scale Carrousel ODs.

Risk Evaluation of Maintenance Scheme Based on PP-AGA

A new evaluation model for maintenance scheme is proposed in this paper, which is based on Projection Pursuit (PP) and Accelerating Genetic Algorithm (AGA). Through this model, the data-driven combined weights of nature states are obtained. These combined weights take full advantage of the decision-making information of the proflt and loss matrix, thus, the risk probability and risk results can be combined efiectively. This evaluation model takes full consideration of the potential risks in the maintenance scheme accurately. Results of the case analysis show that the method is feasible and efiective.

Optimizing multi-variables of microbial fuel cell for electricity generation with an integrated modeling and experimental approach

Microbial fuel cell (MFC) is a device that transforms chemical energy in wastewater into electricity, and its performance is influenced by multi-variables. Mathematic modeling approach could be a useful alternative to design and optimize such a complex system for power generation and wastewater treatment. Here we develop a novel integrated modeling approach with uniform design (UD), a machine learning approach of relevance vector machine (RVM) and a global searching algorithm of accelerating genetic algorithm (AGA) to optimize the operation of multi-variable MFCs after they are constructed. With the integrated UD–RVM–AGA approach, a maximum Coulombic efficiency of 73.0% and power density of 1097mW/m3 of MFC are estimated under the optimal conditions of ionic concentration of 102mM, initial pH of 7.75, medium nitrogen concentration of 48.4mg/L, and temperature of 30.6°C. The Coulombic efficiency and power density in the verification experiments, 70.9% and 1156mW/m3, are close to those calculated by the modeling approach. The results demonstrate that the integrated UD–RVM–AGA approach is effective and reliable to optimize the complex MFC and improve its performance.

A simulation-based integrated approach to optimize the biological nutrient removal process in a full-scale wastewater treatment plant

The biological nutrient removal in anaerobic–anoxic–oxic (A/A/O) process in a full-scale wastewater treatment plant is simulated and later optimized by means of an integrated system model, which combines an extended model derived from activated sludge model No. 3 with support vector machine (SVM) and accelerating genetic algorithm (AGA). The data generated by the mechanistic model are used for establishing the relationship between the operating conditions and effluent water quality by SVM. The AGA approach is used to optimize the operating conditions by taking into account the effluent quality. Through the optimization of the integrated model, the volume of the anoxic tank could be reduced by about 11% and the internal recycle ratio could be decreased from 300% to 250–260%, while the effluent quality would still meet the discharging standards. The results demonstrate that the integration of the mechanistic model, SVM model and AGA approach is an effective strategy for the optimization of complex biological processes.

Simulation and optimization of a full-scale Carrousel oxidation ditch plant for municipal wastewater treatment

A full-scale Carrousel oxidation ditch wastewater treatment plant (WWTP) was simulated and optimized through integrating the activated sludge model 2d (ASM2d), support vector regression (SVR) and accelerating genetic algorithm (AGA). The ASM2d model after calibration and validation with the operating data was used to simulate the process. Operating parameters, including hydraulic retention times (HRTs) of anaerobic, anoxic and aerobic tanks, solids retention time (SRT) and internal recirculation ratio were subjected to optimization using SVR and AGA. The simulation results were normalized and SVR was employed to correlate the operating factors and the effluent quality. Then, the AGA approach was used to obtain the optimal operating conditions. The multiple-objective optimization with different weights indexes was adopted to achieve simultaneous nutrient removal. Compared with the present operating conditions, the HRT of the anoxic tank, the internal recirculation ratio and the SRT should be reduced, while the HRT of the aerobic tank should be prolonged to achieve better effluent quality. Such an integrated approach in this study offers an effective and useful tool to optimize the oxidation ditch process of WWTPs.

A Novel Integrated Approach to the Enhanced Production of Polyhydrobutyrate with Mixed Culture in Activated Sludge

Polyhydrobutyrate (PHB) production from fatty acids by activated sludge is mainly influenced by the ratio of initial substrate concentration (S0) to initial biomass concentration (X0), ratio of chemical oxygen demand to nitrogen (COD/N), and aeration strength. In this work the PHB production by activated sludge was evaluated and enhanced using an integration of the response surface methodology (RSM) and a novel machine learning method, that is, support vector regression (SVR) coupled with the accelerating genetic algorithm (AGA). The results demonstrated that the SVR−AGA model was superior over the RSM model. With the SVR−AGA approach, a maximum PHB yield of 185.48 mg PHB/g VSS under the optimum conditions of S0/X0 of 0.286, COD/N of 144, and aeration intensity 0.004 m3/(L·h) was estimated. The verification experiment confirmed that the measured optimum PHB yield was close to that estimated by using the SVR−AGA approach. This work indicates that an integration of the SVR and AGA methods was an appropriate...

Application of integer coding accelerating genetic algorithm in rectangular cutting stock problem

An improved genetic algorithm and its application to resolve cutting stock problem are presented. It is common to apply simple genetic algorithm (SGA) to cutting stock problem, but the huge amount of computing of SGA is a serious problem in practical application. Accelerating genetic algorithm (AGA) based on integer coding and AGA's detailed steps are developed to reduce the amount of computation, and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem. SGA is adopted to produce individuals within given evolution process, and the variation interval of these individuals is taken as initial domain of the next optimization process, thus shrinks searching range intensively and accelerates the evaluation process of SGA. To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result, fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process. According to the computational experiment, it is observed that this improved GA converges much sooner than SGA, and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem.