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Simulation of anaerobic digestion processes using stochastic algorithm

JOURNAL OF ENVIRONMENTAL HEALTH SCIENCE AND ENGINEERING 2014年第1期12卷 1-8页

作者： Palanichamy, Jegathambal Palani, Sundarambal Karunya Univ Water Inst Coimbatore 641114 Tamil Nadu India Natl Univ Singapore Trop Marine Sci Inst Singapore 119227 Singapore

Background: The Anaerobic Digestion (AD) processes involve numerous complex biological and chemical reactions occurring simultaneously. Appropriate and efficient models are to be developed for simulation of anaerobic digestion systems. Although several models have been developed, mostly they suffer from lack of knowledge on constants, complexity and weak generalization. The basis of the deterministic approach for modelling the physico and bio-chemical reactions occurring in the AD system is the law of mass action, which gives the simple relationship between the reaction rates and the species concentrations. The assumptions made in the deterministic models are not hold true for the reactions involving chemical species of low concentration. The stochastic behaviour of the physicochemical processes can be modeled at mesoscopic level by application of the stochastic algorithms. Method: In this paper a stochastic algorithm (Gillespie Tau Leap Method) developed in MATLAB was applied to predict the concentration of glucose, acids and methane formation at different time intervals. By this the performance of the digester system can be controlled. The processes given by ADM1 (Anaerobic Digestion Model 1) were taken for verification of the model. Results: The proposed model was verified by comparing the results of Gillespie's algorithms with the deterministic solution for conversion of glucose into methane through degraders. At higher value of 'tau' (timestep), the computational time required for reaching the steady state is more since the number of chosen reactions is less. When the simulation time step is reduced, the results are similar to ODE solver. Conclusion: It was concluded that the stochastic algorithm is a suitable approach for the simulation of complex anaerobic digestion processes. The accuracy of the results depends on the optimum selection of tau value.

关键词： Anaerobic digestion stochastic algorithm Gillespie algorithm Modeling

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IQ-TREE: A Fast and Effective stochastic algorithm for Estimating Maximum-Likelihood Phylogenies

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MOLECULAR BIOLOGY AND EVOLUTION 2015年第1期32卷 268-274页

作者： Lam-Tung Nguyen Schmidt, Heiko A. von Haeseler, Arndt Bui Quang Minh Med Univ Vienna Univ Vienna Max F Perutz Labs Ctr Integrat Bioinformat Vienna Vienna Austria Univ Vienna Fac Comp Sci Vienna Austria

Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%. IQ-TREE is freely available at http://***/software/iqtree.

关键词： phylogenetic inference phylogeny maximum likelihood stochastic algorithm

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Parsimonious modelling allows generation of the dendrograms of primate striatal medium spiny and pallidal type II neurons using a stochastic algorithm

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BRAIN RESEARCH 2008年 1238卷 288-300页

作者： Mouchet, Patrick Yelnik, Jerome Univ Joseph Fourier Site Sante Grenoble Inst Neurosci F-38042 Grenoble 9 France Hop La Pitie Salpetriere INSERM U679 Paris France

Data from quantitative three-dimensional analysis of primate striatal medium spiny neurons (MSNs) and pallidal type I and type II neurons were used to search for possible rules underlying the dendritic architecture of these cells. Branching and terminating probabilities per unit length of dendrite were computed from all available measurement points. in the three neuronal groups, terminating probabilities were found to be exponentially increasing functions of the path distance to soma. MSNs and type II branching probabilities could be accurately modelled with decreasing functions of both the metrical (exponential functions) and topological (power functions of the centrifugal branch order) distances to soma. Additionally, type II branching also slightly depended on the distance to the proximal tip of the supporting branches. Type I branching probabilities did not follow these rules accurately. Embedding the modelled probability functions in a stochastic algorithm allowed generation of dendrograms close to those of the real MSNs and pallidal type II neurons, while the algorithm failed to simulate type I dendrites. MSN and pallidal type II neuron branching and terminating probabilities are thus highly dependent on the position in the dendritic arbor. This relationship can be modelled with simple functions and has a strong incidence on the dendrogram structure of the cells concerned. The additional dependence of the branching probability on the within-branch position led us to propose an extension of a previous modelling study by Nowakowski and co-workers which could account for a large range of topological and metrical (length) dendritic tree structures. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Basal Ganglia Parsimonious modelling Quantitative morphology Dendrogram structure stochastic algorithm

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Distributed stochastic algorithm for Global Optimization in Networked System

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2018年第3期179卷 1001-1007页

作者： Wang, Shengnan Li, Chunguang Zhejiang Univ Coll Informat Sci & Elect Engn Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Zhejiang Peoples R China

This paper studies the distributed optimization problem, whose aim is to find the global minimizer of the sum of multiple agents' local nonconvex objective functions in a networked system. To solve such a distributed global optimization problem, we propose a distributed stochastic algorithm and we give detailed analysis of the global convergence of the proposed algorithm.

关键词： Distributed optimization Networked system Global optimization stochastic algorithm

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Efficiency of stochastic algorithm for Different Target Functions in the Task of Estimating Radio Pulse Time Shift 6

Efficiency of Stochastic Algorithm for Different Target Func...

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6th International Conference on Information Technology and Nanotechnology (IEEE ITNT)

作者： Tashlinskii, Alexander Kovalenko, Roman Tsaryov, Mikhail Ulyanovsk State Tech Univ Radio Engn Dept Ulyanovsk Russia

ISBN: (纸本)9781728170411

A comparative analysis of efficiency of different target functions for stochastic algorithms in estimating time shift between unfiltered radio pulses received on spatially spaced receivers is carried out. As target functions, we investigated measures of signals similarity that are heterogeneous in their properties. It includes mean square difference, correlation coefficient, and mutual information. Note that two approaches were used to estimate mutual information. In the first case, the entropy of samples was calculated from histograms, and in the second one, using Parzen windows. The developed algorithms were tested on simulated and real signals. algorithms showed high efficiency.

关键词： stochastic algorithm mean square difference correlation coefficient mutual information time shift

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A novel node-based non-structural model for mass exchanger network synthesis using a stochastic algorithm

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JOURNAL OF CLEANER PRODUCTION 2022年第0期376卷

作者： Zhou, Zhiqiang Cui, Guoming Xiao, Yuan Univ Shanghai Sci & Technol Sch Energy & Power Engn 516 Jungong Rd Shanghai 200093 Peoples R China

The use of mass exchange networks is an effective means of reducing both pollutant emissions and investment costs in chemical industrial processes. Here, a new simultaneous model based on a mixed integer non-linear program called the node-based non-structural model is proposed for the synthesis of mass exchange networks. This model uses the concept of nodes in both rich and lean streams to represent the possible generation positions of mass exchange units, with no fixed matching pattern. The presented model is more random and flexible in forming structures compared to other structural models with fixed matching relationships. These characteristics, in addition to the adoption of non-iso-compositional mixing, resulted in the search region of the proposed model being significantly enlarged. The established model was also modified to incorporate networks with regenera-tion. However, the high nonlinearity and nonconvexity of studied problems make it difficult to obtain a global optimal network design. To overcome this, a random walk algorithm with compulsive evolution, a kind of stochastic algorithm first used in solving heat exchange network problems, was applied for the solution. The algorithm's strength is its feature of independent evolutionary strategy among individuals, which can optimize both the integer and continuous variables simultaneously. The effectiveness of the proposed method was demonstrated by solving four different examples from the literature. For each of the four examples, a lower total annual cost result which satisfied all kinds of constraints was obtained compared to previous studies.

关键词： Mass exchanger networks Process integration Simultaneous method Non-structural model stochastic algorithm

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Distributed stochastic Proximal algorithm With Random Reshuffling for Nonsmooth Finite-Sum Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 4082-4096页

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Xie, Lihua Beijing Inst Technol Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200082 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Beijing Inst Technol Key Lab Biomimet Robots & Syst Minist Educ Beijing 100081 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The nonsmooth finite-sum minimization is a fundamental problem in machine learning. This article develops a distributed stochastic proximal-gradient algorithm with random reshuffling to solve the finite-sum minimization over time-varying multiagent networks. The objective function is a sum of differentiable convex functions and nonsmooth regularization. Each agent in the network updates local variables by local information exchange and cooperates to seek an optimal solution. We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution with an O((1/T)+(1/root T)) convergence rate, where T is the total number of iterations. Finally, some comparative simulations are provided to verify the convergence performance of the proposed algorithm.

关键词： Distributed optimization proximal operator random reshuffling (RR) stochastic algorithm time-varying graphs

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A stochastic EM algorithm FOR MIXTURES WITH CENSORED-DATA

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 1995年第1期46卷 1-25页

作者： CHAUVEAU, D UNIV MARNE VALLEE EQUIPE ANAL & MATH APPLF-93166 NOISY LE GRANDFRANCE TEXAS TECH UNIV LUBBOCKTX 79409

The stochastic EM algorithm is a widely applicable approach for computing maximum likelihood estimates for the mixture problem. We present here an extension of the SEM algorithm in a particular case of incomplete data, where the loss of information is due both to mixture models and censored observations. We propose several solutions to implement the 'SEMcm algorithm' (SEM for censored mixture), showing in particular that one of these procedures solves numerical problems arising with the EMcm algorithm and mixtures of nonexponential-type distributions. Theoretically, we study the asymptotic behavior of SEMcm in the simple case of a two-component censored mixture, where the unknown parameter is the mixing proportion. We prove, for each SEMcm procedures, convergence of the stationary distribution to a Gaussian distribution located on the m.l.e. of the parameter. To conclude, we give some examples based on simulations for censored samples with a great amount of lost information.

关键词： CENSORED DATA EM algorithm FINITE MIXTURE MAXIMUM LIKELIHOOD stochastic algorithm

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A toolbox using the stochastic optimization algorithm MIPT and ChemCAD for the systematic process retrofit of complex chemical processes

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COMPUTERS & CHEMICAL ENGINEERING 2016年 84卷 371-381页

作者： Otte, Daniel Lorenz, Hilke-Marie Repke, Jens-Uwe TU Bergakademie Freiberg D-09599 Freiberg Germany

Global optimization techniques using powerful algorithms have led to a wide range of applications to increase the efficiency of chemical processes. Nevertheless, the performance for optimization of process models is limited by a certain complexity, especially accounting for existing processes (retrofit). Due to the great combinatorial diversity of possible alternatives a systematic approach is essential. The local integration of modifications in the overall process leads to changes in internal streams. Therefore new operating points have to be found and resulting effects on the plant performance have to be evaluated. An optimization framework for the purpose of retrofitting using a rigorous process simulation tool is proposed to fulfill this task. Here, flowsheet simulation software packages are offering a high performance for the prediction of new operation points for following units, and for units affected by recycle streams. An optimization approach using flowsheet simulation software and the stochastic optimization algorithm Molecular-Inspired Parallel Tempering (MIPT) implemented in the programming software Matlab (TM) is presented. Both of these programs are linked via OPC (OLE for process control), a standard communication platform. The toolbox provides a quick evaluation of the process by searching for the global optimum. The MIPT algorithm is suitable for large optimization problems and can handle constraints and infeasibilities. The usage of a rigorous process simulator is providing a high accuracy of the thermodynamic results which is necessary to evaluate the influence of the new process design. Furthermore, a simulation model of the industrial plant can directly be used for the optimization. A complex multicomponent separation process with recycle streams is used to demonstrate the advantages of the proposed toolbox. To simplify the user input a graphical user interface was programmed. The results of a sensitivity analysis and the optimization for diffe

关键词： Optimization Molecular-Inspired Parallel Tempering Multicomponent separation process Retrofit stochastic algorithm ChemCAD (TM)

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COMPETITIVE INTERACTIONS DURING DENDRITIC GROWTH - A SIMPLE stochastic GROWTH algorithm

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BRAIN RESEARCH 1992年第1期576卷 152-156页

作者： NOWAKOWSKI, RS HAYES, NL EGGER, MD UNIV MED & DENT NEW JERSEY ROBERT WOOD JOHNSON MED SCHDEPT NEUROSCI & CELL BIOLPISCATAWAYNJ 08854

A simple growth algorithm is presented that deals with one feature of dendritic growth, the distance between branches. The fundamental assumption of our growth algorithm is that the lengths of dendritic segments are determined by the branching characteristics of the growing neurite. Realistic-appearing dendritic trees are produced by computer simulations in which it is assumed that: (1) growth of individual neurons occurs only at the tips of each growing neurite;(2) the growing neurite can either branch (as a bifurcation) or continue to elongate;(3) events at any one growing tip do not affect the events at any other growing tip;and (4) the probability of branching is a function only of the distance grown either from the cell body (if branching has not occurred) or from the previous branch point. An analytic solution of a differential equation based on these same assumptions produces a distribution of dendritic segment lengths that accurately fits an experimentally determined distribution of dendritic segment lengths of reconstructed neurons, accounting for about 89% of the sample variance. Our analysis indicates that, immediately following branching, the temporary suppression of further branching during dendritic growth may be an important mechanism for regulating the distance between branches.

关键词： COMPUTER SIMULATION DENDRITE DEVELOPMENT DORSAL HORN NEURON GROWTH NEURONAL FORM NEURONAL RECONSTRUCTION stochastic algorithm

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