Modeling of enhanced VFAs production from waste activated sludge by modified ADM1 with improved particle swarm optimization for parameters estimation

Bai, Jie; Liu, He; Yin, Bo; Ma, Huijun

doi:10.1016/j.bej.2015.06.015

Cited by 24 publications

(4 citation statements)

References 34 publications

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“…However the convergence rate decreased when reaching a near optimal solution, which will stop the optimizing. In the past few decades, many researches are attempted to improve the performance of PSO from the following aspects: 1) Improving value of the inertia weights [11][12][13][14]; 2) Increasing the diversity of PSO [15][16][17]; 3) Hybrid PSO with other intelligent optimization algorithm [18,19].…”

Section: The Proposed Social Emotional Pso (Sepso)mentioning

confidence: 99%

Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

Zheng

Liao

2016

Optik

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Section: The Proposed Social Emotional Pso (Sepso)mentioning

confidence: 99%

Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

Zheng

Liao

2016

Optik

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“…The method was stable during changes in number of parameters [55]. Offline estimation of yield and kinetic coefficients in anaerobic wastewater treatment by reducing the error between actual measured response and simulated response has been done using PSO [56], and PSO has been used for parameter estimation in a modified ADM1 model for modeling volatile fatty acids (VFA), showing its advantage in directly seeking the optima in a multidimensional space without crossover and mutation [57]. Multilayer perceptron neural network (MLPNN) and PSO were combined [58] to obtain maximum methane percentage in biogas, biogas quantity, and biogas quality.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

2019

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Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling.

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“…Faktor lain yang menyebabkan inakurasi perhitungan adalah pengabaian beberapa kondisi, seperti inhibisi pH dan proses fisikokimia. Akurasi dapat diperbaiki dengan mengoptimasi parameter yang digunakan dengan metode optimasi [13], [14]. Uji T menghasilkan nilai p 0.40 pada tingkat kepercayaan 95% sehingga mengindikasikan bahwa variansi nilai metana hasil perhitungan dan data eksperimen berbeda.…”

Section: Metode Penelitianunclassified

Implementasi Metode Runge-Kutta-Gill pada Anaerobic Digestion Model No. 1 untuk Memodelkan Produksi Biogas

Dewantara

2017

IndoJC

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Penelitian ini dilakukan untuk membuat model prediksi terhadap hasil biogas yang didapatkan dengan menggunakan reaktor tipe batch. Proses simulasi tersebut dapat menggunakan Anaerobic Digestion Model (ADM1) dengan menggunakan konsentrasi awal glukosa sebesar 500 mgCOD/l dan konsentrasi awal mikroba sebesar 30 mgCOD/l selama 106 jam. Pada penelitian ini bertujuan untuk mengetahui profil kinetika reaksi yang terlibat, penentuan akurasi perhitungan, pengaruh nilai pembagi interval terhadap waktu perhitungan, dan pengaruh konsentrasi awal pada substrat glukosa dan mikroba. Metode yang digunakan untuk memodelkan produksi biogas adalah Runge Kutta Gill. Hasil simulasi menunjukkan bahwa metana yang dihasilkan dari proses tersebut sebesar 417,49250 mgCOD/l dengan jumlah pembagi interval sebanyak 197.003. Selain itu, jumlah konsentrasi mikroba glukosa merupakan yang terbesar dibanding mikroba lainnya, sebesar 77,66615 mgCOD/l. Nilai parameter yang disarankan pada ADM1 hanya cocok untuk lama produksi kurang dari 29 jam. Konsentrasi awal substrat glukosa dan mikroba berpengaruh terhadap jumlah metana yang dihasilkan. Namun pada konsentrasi awal mikroba yang lebih dari 30 mgCOD/l maka akan menghasilkan jumlah metana yang cenderung konstan.

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Modeling of enhanced VFAs production from waste activated sludge by modified ADM1 with improved particle swarm optimization for parameters estimation

Cited by 24 publications

References 34 publications

Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

Implementasi Metode Runge-Kutta-Gill pada Anaerobic Digestion Model No. 1 untuk Memodelkan Produksi Biogas

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