Multi‐objective optimisation for design and operation of anaerobic digestion using <scp>GA‐ANN</scp> and <scp>NSGA‐II</scp>

Huang, Mingzhi; Wang, Han; Wan, Jinquan; Ma, Yuanyuan; Chen, Xiaohong

doi:10.1002/jctb.4568

Cited by 52 publications

(12 citation statements)

References 27 publications

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“…The successful application of various widespread modeling and optimizing approaches has been already investigated and reported by many researchers in the design of catalysts [53][54][55][56][57]. So far, artificial neural networks (ANN) and support vector machines (SVM) are the most universal because of their wide range of suitability, particularly in nonlinear catalytic phenomena for finding correlations.…”

Section: Discussionmentioning

confidence: 99%

Screening of Additives to Ni-Based Methanation Catalyst for Enhanced Anti-Sintering Performance

Han

Zhao

et al. 2019

Catalysts

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The resistance to sintering of Ni/Al 2 O 3 catalysts with different additives for methanation reaction was modeled and predicted by data mining. In the screening, the resistance to sintering of Na, Ca, Ce, Mg, La, Cu, Zn, Zr, In, Mo, and Ti promoted Ni/Al 2 O 3 catalyst were measured in terms of the increased rate of the size of the metallic nickel particles. The resistance to sintering of catalysts, described by the increased rate of Ni particle size as well as basic physicochemical properties of the 11 selected elements, was adopted for optimization model construction by data mining. Through regression model prediction and experimental verification, Cs was found to be an additive, and promotes the resistance to sintering mostly for Ni/Al 2 O 3 catalysts. This result provides further evidence that data mining techniques can be employed as a highly efficient tool for the discovery of new catalysts in comparison with the traditional experimental method.Catalysts 2019, 9, 493 2 of 15 resistance on the surface, strongly affects the catalytic performance, which increases activity and stability for methanation [24,25]. However, methanation, as a key step of SNG production, is a highly exothermic reaction. For each 1% CO and each 1% CO 2 , the representative methanation gas component temperature rise in ammonia plants is 74 and 60 • C, separately [26]. In the high-temperature reaction environment, the Ni-based methanation catalyst is prone to sintering, resulting in the accelerated migration and aggregation of the nickel microcrystalline particles, and the decrease of the nickel dispersion and the effective specific surface area [27,28]. Al 2 O 3 support also suffers the disadvantage of poor thermal stability. Thus, the catalytic activity is reduced. Therefore, solving the catalyst sintering problem is the biggest challenge for the methanation process.Many efforts have been devoted to further increasing the performance of resistance to sintering of Ni/Al 2 O 3 catalyst. The sintering rate of the catalyst is related to the size and distribution of the nickel crystallites, the structure, morphology and transition state of the support [26]. Adding additives is one of the most effective means to improve the anti-sintering performance of the catalyst. For example, adding ZrO 2 can sequester NiO and γ-Al 2 O 3 and weaken their interactions, which play the role of limiting the aggregation of γ-Al 2 O 3 [17]. The addition of Rh and Ru was reported to improve the heat stability of nickel loading on alumina [29,30]. Adding La 2 O 3 [1], MgO [31] can form LaAlO 3 and MgAl 2 O 4 surface layers and prevent their activity component Ni 2+ diffused into the Al 2 O 3 bulk phase to form NiAl 2 O 4 , slowing down the catalyst sintering rate. CeO 2 was added to inhibit the migration of Ni atoms on the surface of Al 2 O 3 and improve the dispersion of nickel particles owing to the strong metal-support interaction (SMSI) [32]. Recently, Ma et al [33] and Bao and coworkers [34] reported that encapsulating Ni particles with graphene or hexago...

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Section: Discussionmentioning

confidence: 99%

Screening of Additives to Ni-Based Methanation Catalyst for Enhanced Anti-Sintering Performance

Han

Zhao

et al. 2019

Catalysts

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“…Transparency is important because transparent models are very beneficial for controlling and gaining insight into the modelling procedures of AD processes. Although the Genetic Algorithm, which is very similar to the GP, has been applied for modelling AD processes, there is no soft sensor designed with the GP technique in this context [16,17]. Therefore, in this study, we discuss using the GP model for designing more transparent and interpretable soft sensors.…”

Section: Introductionmentioning

confidence: 99%

Robust Data-Driven Soft Sensors for Online Monitoring of Volatile Fatty Acids in Anaerobic Digestion Processes

et al. 2020

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The concentration of volatile fatty acids (VFAs) is one of the most important measurements for evaluating the performance of anaerobic digestion (AD) processes. In real-time applications, VFAs can be measured by dedicated sensors, which are still currently expensive and very sensitive to harsh environmental conditions. Moreover, sensors usually have a delay that is undesirable for real-time monitoring. Due to these problems, data-driven soft sensors are very attractive alternatives. This study proposes different data-driven methods for estimating reliable VFA values. We evaluated random forest (RF), artificial neural network (ANN), extreme learning machine (ELM), support vector machine (SVM) and genetic programming (GP) based on synthetic data obtained from the international water association (IWA) Benchmark Simulation Model No. 2 (BSM2). The organic load to the AD in BSM2 was modified to simulate the behavior of an anaerobic co-digestion process. The prediction and generalization performances of the different models were also compared. This comparison showed that the GP soft sensor is more precise than the other soft sensors. In addition, the model robustness was assessed to determine the performance of each model under different process states. It is also shown that, in addition to their robustness, GP soft sensors are easy to implement and provide useful insights into the process by providing explicit equations.

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“…Because of the nonlinearity, uncertainty, and posterity of the anaerobic treatment process, it is di cult to operate and control that process. To increase the steadiness and reliability of the anaerobic treatment process, modeling is a signi cant method, which can be used in controlling, operation, and optimization of the anaerobic treatment process at a reasonable cost [7]. In recent years, numerous studies have been carried out and various modeling methods have been developed to control and simulate the anaerobic treatment process [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full‐Scale Anaerobic Wastewater Treatment Process

Huang¹,

et al. 2019

Complexity

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The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm (GA-FWNN) that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full-scale anaerobic wastewater treatment process. Moreover, the dataset was preprocessed via a self-adapted fuzzy c-means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA-FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas (methane) production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system.

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Multi‐objective optimisation for design and operation of anaerobic digestion using GA‐ANN and NSGA‐II

Cited by 52 publications

References 27 publications

Screening of Additives to Ni-Based Methanation Catalyst for Enhanced Anti-Sintering Performance

Screening of Additives to Ni-Based Methanation Catalyst for Enhanced Anti-Sintering Performance

Robust Data-Driven Soft Sensors for Online Monitoring of Volatile Fatty Acids in Anaerobic Digestion Processes

A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full‐Scale Anaerobic Wastewater Treatment Process

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