Application of the Simultaneous Perturbation Stochastic Approximation Algorithm for Process Optimization

2020 ◽  
pp. 315-340
Author(s):  
Juan Carlos Castillo Garcia ◽  
Jesús Everardo Olguín Tiznado ◽  
Claudia Camargo Wilson ◽  
Juan Andrés López Barreras ◽  
Rafael García Martínez
Keyword(s):  
Experimental Design ◽  
Approximation Algorithm ◽  
Process Optimization ◽  
Stochastic Approximation ◽  
Industrial Processes ◽  
Linear Search ◽  
Surface Response Methodology ◽  
Surface Response ◽  
Alternative Techniques

There are different techniques for the optimization of industrial processes that are widely used in industry, such as experimental design or surface response methodology to name a few. There are also alternative techniques for optimization, like the Simultaneous Perturbation Stochastic Approaches (SPSA) algorithm. This chapter compares the results that can be obtained with classical techniques against the results that alternative linear search techniques such as the Simultaneous Perturbation Stochastic Approaches (SPSA) algorithm can achieve. Authors start from the work reported by Gedi et al. 2015 to implement the SPSA algorithm. The experiments allow authors to affirm that for this case study, the SPSA is capable of equalizing, even improving the results reported by the authors.

Production of Lipase from Candida rugosa Using Cheese Whey through Experimental Design and Surface Response Methodology

2010 ◽  
Vol 4 (8) ◽  
pp. 1473-1481 ◽  
Author(s):  
Giovana Tommaso ◽  
Bruna Souza de Moraes ◽  
Gabriela Cruz Macedo ◽  
Guilherme Sousa Silva ◽  
Eliana Setsuko Kamimura
Keyword(s):  
Experimental Design ◽  
Cheese Whey ◽  
Candida Rugosa ◽  
Surface Response Methodology ◽  
Surface Response ◽  
Lipase From Candida Rugosa

The impact of acid hydrolysis conditions on carbohydrate determination in lignocellulosic materials: a case study with Eucalyptus globulus bark

Holzforschung ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ricardo Jorge Oliveira ◽  
Bruna Santos ◽  
Maria J. Mota ◽  
Susana R. Pereira ◽  
Pedro C. Branco ◽  
...  
Keyword(s):  
Acid Hydrolysis ◽  
Eucalyptus Globulus ◽  
Industrial Processes ◽  
Lignocellulosic Materials ◽  
Optimal Conditions ◽  
Hydrolysis Conditions ◽  
The Impact ◽  
Eucalyptus Bark

Abstract Lignocellulosic biomass represents a suitable feedstock for production of biofuels and bioproducts. Its chemical composition depends on many aspects (e.g. plant source, pre-processing) and it has impact on productivity of industrial bioprocesses. Numerous methodologies can be applied for biomass characterisation, with acid hydrolysis being a particularly relevant step. This study intended to assess the most suitable procedures for acid hydrolysis, taking Eucalyptus globulus bark as a case study. For that purpose, variation of temperature (90–120 °C) was evaluated over time (0–5 h), through monosaccharides and oligosaccharides contents and degradation. For glucose, the optimal conditions were 100 °C for 2.5 h, reaching a content of 48.6 wt.%. For xylose, the highest content (15.2 wt.%) was achieved at 90 °C for 2 h, or 120 °C for 0.5 h. Maximum concentrations of mannose and galactose (1.0 and 1.7 wt.%, respectively) were achieved at 90 and 100 °C (2–3.5 h) or at 120 °C (0.5–1 h). These results revealed that different hydrolysis conditions should be applied for different sugars. Using this approach, total sugar quantification in eucalyptus bark was increased by 4.3%, which would represent a 5% increase in the ethanol volume produced, considering a hypothetical bioethanol production yield. This reflects the importance of feedstock characterization on determination of economic viability of industrial processes.

scholarly journals An Introduction to Development of Centralized and Distributed Stochastic Approximation Algorithm with Expanding Truncations

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 174
Author(s):  
Wenxiao Zhao
Keyword(s):  
Approximation Algorithm ◽  
Stochastic Approximation ◽  
Probabilistic Method ◽  
Principal Component ◽  
Stochastic Approximation Algorithm ◽  
The Past ◽  
Systems And Control ◽  
Ode Method ◽  
Centralized Algorithm ◽  
And Control

The stochastic approximation algorithm (SAA), starting from the pioneer work by Robbins and Monro in 1950s, has been successfully applied in systems and control, statistics, machine learning, and so forth. In this paper, we will review the development of SAA in China, to be specific, the stochastic approximation algorithm with expanding truncations (SAAWET) developed by Han-Fu Chen and his colleagues during the past 35 years. We first review the historical development for the centralized algorithm including the probabilistic method (PM) and the ordinary differential equation (ODE) method for SAA and the trajectory-subsequence method for SAAWET. Then, we will give an application example of SAAWET to the recursive principal component analysis. We will also introduce the recent progress on SAAWET in a networked and distributed setting, named the distributed SAAWET (DSAAWET).

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