scholarly journals Optimizing the Sodium Hydroxide Conversion Using Regression Analysis in CSTR

2021 ◽  
Vol 11 (15) ◽  
pp. 6789
Author(s):  
Mohammed K. Al Mesfer

The current study deals with the maximization of NaOH conversion using step-wise regression analysis in a CSTR. The dependence of temperature, volume, agitation rate, and feed rate on reactor performance is examined as well as interaction outcome of the operating parameters. The concentration of the reactants was fixed at 0.1 M. The steady state conversion with respect to NaOH is analyzed to find the process performance. Step-wise regression analysis is used to remove an insignificant factors. The agitation rate (X2) and feed rate (X3) proved to have an insignificant influence on the reaction conversion at a significant level (α) of 5%. Consequently, the temperature (X1) and reaction volume (X4) were found to have significant effect on the reaction conversion using step-wise regression. The temperature and volume dependence on steady state NaOH conversion were described by a polynomial model of 2nd and 3rd order. A maximal steady state conversion equal to 63.15% was obtained. No improvement was found in reaction conversion with 3rd order polynomial, so the second order polynomial is considered as the optimum reaction conversion modal. It may be recommended that 2nd order regression polynomial model adequately represents the experimental data very well.

1973 ◽  
Author(s):  
Robert H. Kadlec ◽  
Everett A. Sondreal ◽  
Donald J. Patterson ◽  
Marshall W. Graves

T-Comm ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 18-25
Author(s):  
Alina A. Sherstneva ◽  

The article aims to consider least squares approach for solving problems of queuing systems theory. The opportunity of predicting the behavior of infocommunication system is shown. Choosing the optimal model of its functioning is proposed. On base monitoring system metrics, statistical data were formed. The article proposes to make data trend forecasting, to estimate parameters of random processes over time. To obtain the results of functioning data in infocommunication systems that are as close as possible to the real values, polynomial and sine models are considered. The method of regression analysis is proposed to determine the parameter values for a model from a set of observational data. In theoretical research, the linear and nonlinear least squares methods are used in terms of a circle. The task of experimental analysis is to obtain an estimated parameter of sine, polynomial models and the center of circle. Experimental analysis was performed using the mathematical modeling program Matlab. A uniformly distributed random sequence and a random sequence with normal distribution are generated. The sequence with experimental data for polynomial and sine models, respectively, are calculated. The correspondence each model for generated data is shown in graphical form. The measurement data obeys observations. The estimated parameters are summarized in the tables. The polynomial order is estimated. The estimated dispersion curve of the polynomial model is obtained. The calculated variance values of the polynomial model are presented. Data trend forecasting for measurement data is made. The estimated values are extremally close to real data. The results are shown in graphs. Finally, an approximate model of the circumference of measurement data is presented in graphical form. After some iterations with estimated center from the arithmetic mean the new circle center is given. And quite close values for center and radius of circle are obtained.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1014-1014
Author(s):  
Emily Riehm Meier ◽  
Elizabeth C. Wright ◽  
Naomi L.C. Luban ◽  
Jeffery L. Miller

Abstract Abstract 1014 All sickle cell anemia patients (HbSS, SCA) have the same genetic mutation, but the clinical phenotype is highly variable and difficult to predict prior to the onset of disease complications. If severe SCA could be predicted early in life, then disease modifying therapies could be instituted prior to the onset of organ damage. To determine if reticulocyte levels in SCA patients are useful in disease severity prediction, a convenience sample of 50 children with SCA was enrolled in an observational study. After consent and assent were obtained, discarded peripheral blood obtained during routine clinic visits was collected and analyzed within 48 hours of collection and storage at 4°C. Hematologic data, including absolute reticulocyte counts (ARC), was collected using a Sysmex Hematology Analyzer. Clinical events were examined prospectively from the time of enrollment and retrospectively if the patient had events prior to study enrollment. Clinical events included: painful crises requiring hospital admission (VOC), acute chest syndrome, and splenic sequestration that occurred prior to the onset of chronic therapy. ARC and hematologic data were collected over time and analyzed using Cox regression analysis to determine the relationship between ARC levels and time to the first event. To evaluate the utility of ARC in risk stratification, patients were divided into two groups: ARC less than 200K/uL (ARC<200) and ARC greater than or equal to 200K/uL (ARC≥200). Initial analyses were performed using steady state ARCs prior to the first clinical event (pre-event baseline ARC). Steady state was defined as a sample collected at least 30 days from an acute illness and at least 60 days since the patient received a blood transfusion. Patients were followed an average of 6.7 years (range 0.82–16.8 years), which provides 332 person years of follow-up. A time dependent Cox regression analysis of pre-event baseline ARC≥200 compared with ARC<200 over the first 3 years of life generated a hazard ratio of having a first clinical event of 4.7 [95%CI 1.83–12.29 (p=0.0013)]. Maximum ARC before age 6 months (defined as the infant baseline ARC) was utilized for additional analyses. Cox regression analysis revealed that those subjects with an infant baseline ARC≥200 had 3.2 times the risk of having an event within the first 3 years of life than the group with an infant baseline ARC<200 (HR 3.15, 95%CI 1.54–6.45, p=0.0017). Forty-eight percent (12/25) of patients with an infant baseline ARC <200 had an event by age 3 years compared to 88% (22/25) of patients in the ARC≥200 group (p=0.001). The number of events per patient years was higher in the infant baseline ARC≥200 group (0.74 events/patient years of follow up vs. infant baseline ARC<200, 0.29 events/patient years of follow up, p=0.0004). The median time to first event in the infant baseline ARC≥200 group was shorter [1.39 years (95% CI 0.87–1.93)] than the baseline ARC<200 group [3.06 years (95%CI 1.71–3.80)]. These data suggest that both pre-event and infant baseline ARCs assist with risk stratification in infants and young children with SCA. Further studies are needed to determine if ARC risk stratification assessments are sufficiently robust for the guidance of treatment decisions for pediatric SCA. Disclosures: No relevant conflicts of interest to declare.


Author(s):  
Ali Alizadeh ◽  
Navid Mostoufi ◽  
Farhang Jalali-Farahani

An industrial steam reformer of a methanol plant was modeled at a dynamic condition in which a one dimensional homogeneous model was coupled with a verified kinetics from the literature. A close agreement was observed between the results of the model and industrial data from a real plant at steady state conditions. The open loop response of the system to switching between two operating conditions was investigated and shown that the produced synthesis gas during the transition period would be unsuitable for the downstream methanol converter. The genetic algorithm was then employed to perform a multi-objective dynamic optimization on the reactor performance in case of switching the feed and operating conditions. Maximization of methane conversion and minimization of a stoichiometric parameter, were considered as the two objectives' functions that were optimized for a fixed feed rate of methane to the existing unit. The results of the dynamic optimization for the specified reformer configuration were achieved after switching the operating condition. Results of the optimization showed that the produced synthesis gas would stay in its acceptable limits in terms of quality of the feed of the methanol converter and also, the final conversion of the reformer would be improved compared to the steady state condition. This procedure could be applied to the advanced process control of the methanol plant.


2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Suhaila Mohd Sauid ◽  
Jagannathan Krishnan ◽  
Tan Huey Ling ◽  
Murthy V. P. S. Veluri

Volumetric mass transfer coefficient (kLa) is an important parameter in bioreactors handling viscous fermentations such as xanthan gum production, as it affects the reactor performance and productivity. Published literatures showed that adding an organic phase such as hydrocarbons or vegetable oil could increase thekLa. The present study opted for palm oil as the organic phase as it is plentiful in Malaysia. Experiments were carried out to study the effect of viscosity, gas holdup, andkLaon the xanthan solution with different palm oil fractions by varying the agitation rate and aeration rate in a 5 L bench-top bioreactor fitted with twin Rushton turbines. Results showed that 10% (v/v) of palm oil raised thekLaof xanthan solution by 1.5 to 3 folds with the highestkLavalue of 84.44 h−1. It was also found that palm oil increased the gas holdup and viscosity of the xanthan solution. ThekLavalues obtained as a function of power input, superficial gas velocity, and palm oil fraction were validated by two different empirical equations. Similarly, the gas holdup obtained as a function of power input and superficial gas velocity was validated by another empirical equation. All correlations were found to fit well with higher determination coefficients.


2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Nabeel H. Alharthi ◽  
Sedat Bingol ◽  
Adel T. Abbas ◽  
Adham E. Ragab ◽  
Ehab A. El-Danaf ◽  
...  

In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values. Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.


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