Methods for anaerobic digestion model fitting-comparison between heuristic and automatic approach

被引:8
作者
Postawa, Karol [1 ]
Szczygiel, Jerzy [1 ]
Kulazynski, Marek [1 ]
机构
[1] Wroclaw Univ Sci & Technol, Dept Chem, Wybrzeze Wyspianskiego 27, PL-50370 Wroclaw, Poland
关键词
Biogas; TPAD; Model; Algorithm; Optimization; RENEWABLE ENERGY; NEURAL-NETWORKS; OPTIMIZATION; ALGORITHM; SOLVERS; SYSTEMS; BIOGAS; TPAD;
D O I
10.1007/s13399-020-00945-1
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The article demonstrates if automatic optimization can be better than manual adjustment. The subject of optimization was the temperature-phased anaerobic digestion (TPAD) model. A selection of 3 parameters per each reactor in the process chain was appointed-reaction rate for propionate conversion, acetate conversion, and hydrolysis. Overall, both methods provided very convergent results. However, the total summary error (TSE) for the automatic algorithm was always moderately lower than for manual-the difference varied between 16.16 and 57.05 percentage points. Although the manual method has significant advantages-adjustment was more homogenous and gave more uniform fitting. Finally, cross-validation was performed to unify the values between the experimental series. The result was a total number of 4 values for each optimized constant-for two temperature points in each of two methods. Due to inconclusive information about the accuracy, averaged values were calculated to use in further researches. The recommendation from this article is to connect the best aspect of both methods to achieve the most accurate results.
引用
收藏
页码:4049 / 4059
页数:11
相关论文
共 33 条
[1]  
Agnarsson J, 2013, TECH REP, DOI 10.13140/rg.2.2.28603.87840
[2]   Optimization of process parameters for enhanced biogas yield from anaerobic co-digestion of OFMSW and bio-solids [J].
Ahmed, Banafsha ;
Tyagi, Vinay Kumar ;
Priyanka ;
Khan, Abid Ali ;
Kazmi, A. A. .
BIOMASS CONVERSION AND BIOREFINERY, 2022, 12 (03) :607-618
[3]   Behind and beyond the MATLAB ODE suite [J].
Ashino, R ;
Nagase, M ;
Vaillancourt, R .
COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2000, 40 (4-5) :491-512
[4]   Green biomass to biogas - A study on anaerobic digestion of residue grass [J].
Bedoic, Robert ;
Cucek, Lidija ;
Cosic, Boris ;
Krajnc, Damjan ;
Smoljanic, Goran ;
Kravanja, Zdravko ;
Ljubas, Davor ;
Puksec, Tomislav ;
Duic, Neven .
JOURNAL OF CLEANER PRODUCTION, 2019, 213 :700-709
[5]   Modelling of two-stage anaerobic digestion using the IWA Anaerobic Digestion Model No. 1 (ADM1) [J].
Blumensaat, F ;
Keller, J .
WATER RESEARCH, 2005, 39 (01) :171-183
[6]   Renewable energy from biogas with reduced carbon dioxide footprint: Implications of applying different plant configurations and operating pressures [J].
Budzianowski, Wojciech M. ;
Postawa, Karol .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 68 :852-868
[7]   A review on anaerobic decomposition and enhancement of biogas production through enzymes and microorganisms [J].
Christy, P. Merlin ;
Gopinath, L. R. ;
Divya, D. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 34 :167-173
[8]   Optimization methodology based on neural networks and self-adaptive differential evolution algorithm applied to an aerobic fermentation process [J].
Dragoi, Elena-Niculina ;
Curteanu, Silvia ;
Galaction, Anca-Irina ;
Cascaval, Dan .
APPLIED SOFT COMPUTING, 2013, 13 (01) :222-238
[9]   Modeling the dynamic performance of full-scale anaerobic primary sludge digester using Anaerobic Digestion Model No. 1 (ADM1) [J].
Ersahin, Mustafa Evren .
BIOPROCESS AND BIOSYSTEMS ENGINEERING, 2018, 41 (10) :1539-1545
[10]   Metaheuristics in combinatorial optimization [J].
Gendreau, M ;
Potvin, JY .
ANNALS OF OPERATIONS RESEARCH, 2005, 140 (01) :189-213