scholarly journals Optimized Dissolved Oxygen Fuzzy Control for Recombinant Escherichia coli Cultivations

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 326
Author(s):  
Rafael Akira Akisue ◽  
Matheus Lopes Harth ◽  
Antonio Carlos Luperni Horta ◽  
Ruy de Sousa Junior
Keyword(s):  
Escherichia Coli ◽  
Decision Tree ◽  
Dissolved Oxygen ◽  
Fuzzy Inference ◽  
Recombinant Escherichia Coli ◽  
Oxygen Flow ◽  
Oxygen Solubility ◽  
Inference System ◽  
Flow Rates ◽  
E Coli

Due to low oxygen solubility and mechanical stirring limitations of a bioreactor, ensuring an adequate oxygen supply during a recombinant Escherichia coli cultivation is a major challenge in process control. Under the light of this fact, a fuzzy dissolved oxygen controller was developed, taking into account a decision tree algorithm presented in the literature, and implemented in the supervision software SUPERSYS_HCDC. The algorithm was coded in MATLAB with its membership function parameters determined using an Adaptive Network-Based Fuzzy Inference System tool. The controller was composed of three independent fuzzy inference systems: Princ1 and Princ2 assessed whether there would be an increment or a reduction in air and oxygen flow rates (respectively), whilst Delta estimated the size of these variations. To test the controller, simulations with a neural network model and E. coli cultivations were conducted. The fuzzification of the decision tree was successful, resulting in smoothing of air and oxygen flow rates and, hence, in an attenuation of dissolved oxygen oscillations. Statistically, the average standard deviation of the fuzzy controller was 2.45 times lower than the decision tree (9.48%). Results point toward an increase in the flow meter lifespan and a possible reduction of the metabolic stress suffered by E. coli during the cultivation.

scholarly journals Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

2011 ◽  
Vol 14 (1) ◽  
pp. 167-179 ◽  
Author(s):  
Vesna Ranković ◽  
Jasna Radulović ◽  
Ivana Radojević ◽  
Aleksandar Ostojić ◽  
Ljiljana Čomić
Keyword(s):  
Experimental Data ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Network ◽  
Inference System ◽  
Fuzzy Models ◽  
Highly Nonlinear ◽  
Input Variables

Predicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gruža Reservoir, Serbia. The fuzzy model was developed using experimental data which were collected during a 3-year period. The input variables analysed in this paper are: water pH, water temperature, total phosphate, nitrites, ammonia, iron, manganese and electrical conductivity. The selection of an appropriate set of input variables is based on the building of ANFIS models for each possible combination of input variables. Results of fuzzy models are compared with measured data on the basis of correlation coefficient, mean absolute error and mean square error. Comparing the predicted values by ANFIS with the experimental data indicates that fuzzy models provide accurate results.

scholarly journals Prediction of vitrinite reflectance values using machine learning techniques: a new approach

2020 ◽  
Author(s):  
Zahra Sadeghtabaghi ◽  
Mohsen Talebkeikhah ◽  
Ahmad Reza Rabbani
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Vitrinite Reflectance ◽  
Empirical Equations ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Rock Eval

AbstractVitrinite reflectance (VR) is considered the most used maturity indicator of source rocks. Although vitrinite reflectance is an acceptable parameter for maturity and is widely used, it is sometimes difficult to measure. Furthermore, Rock-Eval pyrolysis is a current technique for geochemical investigations and evaluating source rock by their quality and quantity of organic matter, which provide low cost, quick, and valid information. Predicting vitrinite reflectance by using a quick and straightforward method like Rock-Eval pyrolysis results in determining accurate and reliable values of VR with consuming low cost and time. Previous studies used empirical equations for vitrinite reflectance prediction by the Tmax data, which was accompanied by poor results. Therefore, finding a way for precise vitrinite reflectance prediction by Rock-Eval data seems useful. For this aim, vitrinite reflectance values are predicted by 15 distinct machine learning models of the decision tree, random forest, support vector machine, group method of data handling, radial basis function, multilayer perceptron, adaptive neuro-fuzzy inference system, and multilayer perceptron and adaptive neuro-fuzzy inference system, which are coupled with evolutionary optimization methods such as grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and genetic algorithm, with four inputs of Rock-Eval pyrolysis parameters of Tmax, S1/TOC, HI, and depth for the first time. Statistical evaluations indicate that the decision tree is the most precise model for VR prediction, which can estimate vitrinite reflectance precisely. The comparison between the decision tree and previous proposed empirical equations indicates that the machine learning method performs much more accurately.

scholarly journals Prediksi Kelulusan Mata Kuliah Menggunakan Hybrid Fuzzy Inference System

2016 ◽  
Vol 2 (2) ◽  
pp. 60
Author(s):  
Abidatul Izzah ◽  
Ratna Widyastuti
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Educational Data Mining ◽  
Fuzzy Rule ◽  
Posttest Score ◽  
Inference System ◽  
Is Implementation ◽  
Attention To Students

AbstrakPerguruan Tinggi merupakan salah satu institusi yang menyimpan data yang sangat informatif jika diolah secara baik. Prediksi kelulusan mahasiswa merupakan kasus di Perguruan Tinggi yang cukup banyak diteliti. Dengan mengetahui prediksi status kelulusan mahasiswa di tengah semester, dosen dapat mengantisipasi atau memberi perhatian khusus pada siswa yang diprediksi tidak lulus. Metode yang digunakan sangat bervariatif termasuk metode Fuzzy Inference System (FIS). Namun dalam implementasinya, proses pembangkitan rule fuzzy sering dilakukan secara random atau berdasarkan pemahaman pakar sehingga tidak merepresentasikan sebaran data. Oleh karena itu, dalam penelitian ini digunakan teknik Decision Tree (DT) untuk membangkitkan rule. Dari uraian tersebut, penelitian bertujuan untuk memprediksi kelulusan mata kuliah menggunakan hybrid FIS dan DT. Data yang digunakan dalam penelitian ini adalah data nilai Posttest, Tugas, Kuis, dan UTS dari 106 mahasiswa Politeknik Kediri pengikut mata kuliah Algoritma dan Struktur Data. Penelitian ini diawali dari membangkitkan 5 rule yang selanjutnya digunakan dalam inferensi. Tahap selanjutnya adalah implementasi FIS dengan tahapan fuzzifikasi, inferensi, dan defuzzifikasi. Hasil yang diperoleh adalah akurasi, sensitivitas, dan spesifisitas  masing-masing adalah 94.33%, 96.55%, dan 84.21%.Kata kunci: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediksi. AbstractCollege is an institution that holds very informative data if it mined properly. Prediction about student’s graduation is a common case that many discussed. Having the predictions of student’s graduation in the middle semester, lecturer will anticipate or give some special attention to students who would be not passed. The method used to prediction is very varied including Fuzzy Inference System (FIS). However, fuzzy rule process is often generated randomly or based on knowledge experts that not represent the data distribution. Therefore, in this study, we used a Decision Tree (DT) technique for generate the rules. So, the research aims to predict courses graduation using hybrid FIS and DT. Dataset used is the posttest score, tasks score, quizzes score, and middle test score from 106 students of the Polytechnic Kediri who took Algorithms and Data Structures. The research started by generating 5 rules by decision tree. The next is implementation of FIS that consist of fuzzification, inference, and defuzzification. The results show that the classifier give a good result in an accuracy, sensitivity, and specificity respectively was 94.33%, 96.55% and 84.21%.Keywords: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediction.

Killing of Escherichia coli using the gas diffusion electrode system

2010 ◽  
Vol 61 (1) ◽  
pp. 107-118
Author(s):  
W. Y. Xu ◽  
P. Li ◽  
B. Dong
Keyword(s):  
Escherichia Coli ◽  
Flow Rate ◽  
Current Density ◽  
Ph Value ◽  
Operating Cost ◽  
Gas Diffusion ◽  
Oxygen Flow Rate ◽  
Gas Diffusion Electrode ◽  
Oxygen Flow ◽  
E Coli

To be best of our knowledge, this study is one of the first investigations to be performed into the potential benefits of gas diffusion electrode (GDE) system in controlling inactivation of E. coli. This study mainly focused on the dual electrodes disinfection with gas diffusion cathode, using Escherichia coli as the indicator microorganisms. The effects of Pt load WPt and the pore-forming agent content WNH4HCO3 in GDE, operating conditions such as pH value, oxygen flow rate QO2, salt content and current density on the disinfection were investigated, respectively. The experimental results showed that the disinfection improved with increasing Pt load WPt, but its efficiency at Pt load of 3‰ was equivalent to that at Pt load of 4‰. Addition of the pore-forming agent in the appropriate amount improved the disinfection while drop of pH value resulted in the rapid rise of the germicidal efficacy and the disinfection shortened with increasing oxygen flow rate QO2. The system is more suitable for highly salt water. The germicidal efficacy increased with current density. However, the accelerating rate was different: it first increased with the current density, then decreased, and reached a maximum at current density of 6.7–8.3 mA/cm2. The germicidal efficacy in the cathode compartment was about the same as in the anode compartment indicating the contribution of direct oxidation and indirect treatment of E. coli by the hydroxyl radical was similar to the oxidative indirect effect of the generated H2O2. This technology is expensive in operating cost, further research is required to advance the understanding and reduce the operating cost of this technology.

scholarly journals Seasonal influence of environmental variables and artificial aeration on Escherichia coli in small urban lakes

2016 ◽  
Vol 14 (6) ◽  
pp. 929-941 ◽  
Author(s):  
Bart W. Durham ◽  
Lucy Porter ◽  
Allie Webb ◽  
Joshua Thomas
Keyword(s):  
Escherichia Coli ◽  
Dissolved Oxygen ◽  
Environmental Variables ◽  
Solar Irradiation ◽  
Primary Control ◽  
Recreational Waters ◽  
Urban Lakes ◽  
E Coli ◽  
Artificial Aeration ◽  
Period 2

This study investigated patterns of Escherichia coli in urban lakes in Lubbock, Texas. Specific objectives were to (1) document seasonal patterns in abundance of E. coli over a 3-year period, (2) identify environmental factors, including effects of migratory geese and artificial aeration devices that may influence E. coli abundance, and (3) determine if E. coli abundance over time was similar for individual lakes. Water samples were collected monthly for 36 months from six lakes, three of which contained artificial aeration devices (fountains). Regression models were constructed to determine which environmental variables most influence E. coli abundance in summer and winter seasons. Escherichia coli is present in the lakes of Lubbock, Texas year-round and typically exceeds established bacterial thresholds for recreational waters. Models most frequently contained pH and dissolved oxygen as predictor variables and explained from 17.4% to 92.4% of total variation in E. coli. Lakes with fountains had a higher oxygen concentration during summer and contained consistently less E. coli. We conclude that solar irradiation in synergy with pH and dissolved oxygen is the primary control mechanism for E. coli in study lakes, and that fountains help control abundance of fecal bacteria within these systems.

Biosynthesis and display of diverse metal nanoparticles by recombinant Escherichia coli

RSC Advances ◽  
2014 ◽  
Vol 4 (102) ◽  
pp. 58717-58719 ◽  
Author(s):  
Yi-Jung Tsai ◽  
Chun-Yu Ouyang ◽  
Shi-Yuan Ma ◽  
Dong-Yu Tsai ◽  
Hsueh-Wei Tseng ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metal Ions ◽  
Metal Nanoparticles ◽  
Recombinant Escherichia Coli ◽  
E Coli

This study used the recombinant E. coli strain expressing the biomolecule, eumelanin, as an agent for the reduction of metal ions.

scholarly journals Extracellular expression of Aerococcus viridans pyruvate oxidase in recombinant Escherichia coli through SecB co-expression

RSC Advances ◽  
2019 ◽  
Vol 9 (45) ◽  
pp. 26291-26301 ◽  
Author(s):  
Junwen Lu ◽  
Jianguo Zhang
Keyword(s):  
Escherichia Coli ◽  
Recombinant Escherichia Coli ◽  
Extracellular Expression ◽  
Pyruvate Oxidase ◽  
E Coli ◽  
Aerococcus Viridans ◽  
High Level

Extracellular pyruvate oxidase was expressed at a high level using E. coli by co-expression of chaperone SecB under bla promoter, and therefore cultivation optimization.

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