MODELING, OPTIMIZATION AND CONTROL OF A FCC UNIT USING NEURAL NETWORKS AND EVOLUTIONARY METHODS

This paper presents a simulation study of the use of an artificial neural network (ANN) model for control and optimization of a Fluidized-Bed Catalytic Cracking reactor-regenerator system (FCC). This case study, whose phenomenological model was validated with industrial data, is a multivariable and nonlinear process with strong interactions among the operational variables. In order to obtain a dynamic model of the FCC system, a feedforward ANN model was identified. Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) evolutionary methods were used to set optimal operating conditions for the FCC, and both algorithms presented good and consistent results for typical FCC optimization problems. The neural model was also used in the design of a Model-Based Predictive Control (MPC) for the FCC process. It was showed that the ANN-based MPC was able to reject the imposed disturbance as well as to track the proposed trajectory, while considering operational constraints of the plant.

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Prediction of Hot Aisle Partition Airflow Boundary Conditions

Volume 2: Thermal Management; Data Centers and Energy Efficient Electronic Systems ◽

10.1115/ipack2013-73049 ◽

2013 ◽

Author(s):

Zhihang Song ◽

Bruce T. Murray ◽

Bahgat Sammakia

Keyword(s):

Boundary Conditions ◽

Data Center ◽

Computation Time ◽

Design Tool ◽

Operating Conditions ◽

Ann Model ◽

Flow Rates ◽

Level Model ◽

Optimization Methodology ◽

Artificial Neural Network Ann

The integration of a simulation-based Artificial Neural Network (ANN) with a Genetic Algorithm (GA) has been explored as a real-time design tool for data center thermal management. The computation time for the ANN-GA approach is significantly smaller compared to a fully CFD-based optimization methodology for predicting data center operating conditions. However, difficulties remain when applying the ANN model for predicting operating conditions for configurations outside of the geometry used for the training set. One potential remedy is to partition the room layout into a finite number of characteristic zones, for which the ANN-GA model readily applies. Here, a multiple hot aisle/cold aisle data center configuration was analyzed using the commercial software FloTHERM. The CFD results are used to characterize the flow rates at the inter-zonal partitions. Based on specific reduced subsets of desired treatment quantities from the CFD results, such as CRAC and server rack air flow rates, the approach was applied for two different CRAC configurations and various levels of CRAC and server rack flow rates. Utilizing the compact inter-zonal boundary conditions, good agreement for the airflow and temperature distributions is achieved between predictions from the CFD computations for the entire room configuration and the reduced order zone-level model for different operating conditions and room layouts.

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Development of a Neural Statistical Model for the Relative Humidity Levels Prediction in the Region of Rabat-Kenitra (Morocco)

10.21203/rs.3.rs-385467/v1 ◽

2021 ◽

Author(s):

Kaoutar Elazhari ◽

Badreddine ABDALLAOUI ◽

Ali DEHBI ◽

Abdelaziz ABDALLAOUI ◽

Hamid ZINEDDINE

Keyword(s):

Relative Humidity ◽

Meteorological Parameters ◽

Transfer Functions ◽

Mean Squared Error ◽

Neural Model ◽

Ann Model ◽

History Of ◽

Artificial Neural Network Ann ◽

Hidden Layer ◽

Mlp Model

Abstract This work provides the development of a powerful artificial neural network (ANN) model, for the prediction of relative humidity levels, using other meteorological parameters of the Rabat-Kenitra region. The treatment was applied to a database containing a daily history of five meteorological parameters of 9 stations covering this region for a period from 1979 to mid-2014. We have shown that for the prediction of relative humidity in this region, the best performing three-layer ANN (input, hidden and output) mathematical model is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, having an architecture [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer was able to estimate values for relative humidity very close to those observed. Indeed, this was affirmed by a low mean squared error (MSE) and a fairly high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.

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Using Neural Network Predictive Control for Riser-Slugging Suppression

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1315 ◽

2009 ◽

Vol 4 (4) ◽

Cited By ~ 1

Author(s):

Reza Eslamloueyan ◽

Elham Hosseinzadeh

Keyword(s):

Neural Network ◽

Control System ◽

Predictive Control ◽

Oil And Gas ◽

Neural Model ◽

Nonlinear Process ◽

Economic Benefits ◽

Gas Production ◽

Ann Model ◽

Oil And Gas Production

Riser-slugging is a flow regime that can occur in multiphase pipeline-riser systems, and is characterized by severe flow and pressure oscillations. Reducing undesired slugging effects can have great economic benefits. Recently, control methods have been proposed to conquer slugging flow problems in pipeline risers. The advantages of using a control system are that it can be installed on existing oil and gas production facilities with no need for expensive equipment and no significant pressure drop is imposed to the system.In this work, a predictive control system based on Neural Network (NN) model of process is developed for handling and suppressing riser-slugging. An ANN model of the plant is used to predict future response of the nonlinear process. Storkaas dynamic model (Storkaas and Skogestad,2002) is employed for the process simulation. Comparing the results of this research to that of others, indicates that the proposed neural model predictive controller makes a significant improvement in the setpoint tracking especially for higher step change in the setpoint value.

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Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

Journal of Nanotechnology in Engineering and Medicine ◽

10.1115/1.4003500 ◽

2011 ◽

Vol 2 (1) ◽

Cited By ~ 5

Author(s):

Hadi Salehi ◽

Mosayyeb Amiri ◽

Morteza Esfandyari

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Energy Gain ◽

Operating Conditions ◽

Maximum Relative Error ◽

Ann Model ◽

Wide Range ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

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Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Energies ◽

10.3390/en11092216 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2216 ◽

Cited By ~ 4

Author(s):

Ravi Kishore ◽

Roop Mahajan ◽

Shashank Priya

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Electrical Energy ◽

Optimization Techniques ◽

Operating Conditions ◽

Ann Model ◽

Predicting Performance ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

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Applying Artificial Intelligence to Predict the Composition of Syngas Using Rice Husks: A Comparison of Artificial Neural Networks and Gradient Boosting Regression

Energies ◽

10.3390/en14102932 ◽

2021 ◽

Vol 14 (10) ◽

pp. 2932

Author(s):

Hung-Ta Wen ◽

Jau-Huai Lu ◽

Mai-Xuan Phuc

Keyword(s):

Artificial Intelligence ◽

Fixed Bed ◽

Operating Conditions ◽

Gradient Boosting ◽

Ann Model ◽

Rice Husks ◽

Ensemble Technique ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Combustible Gases

The purpose of this study is to utilize two artificial intelligence (AI) models to predict the syngas composition of a fixed bed updraft gasifier for the gasification of rice husks. Air and steam-air mixtures are the gasifying agents. In the present work, the feeding rate of rice husks is kept constant, while the air and steam flow rates vary in each case. The consideration of various operating conditions provides a clear comparison between air and steam-air gasification. The effects of the reactor temperature, steam-air flow rate, and the ratio of steam to biomass are investigated here. The concentrations of combustible gases such as hydrogen, carbon monoxide, and methane in syngas are increased when using the steam-air mixture. Two AI models, namely artificial neural network (ANN) and gradient boosting regression (GBR), are applied to predict the syngas compositions using the experimental data. A total of 74 sets of data are analyzed. The compositions of five gases (CO, CO2, H2, CH4, and N2) are predicted by the ANN and GBR models. The coefficients of determination (R2) range from 0.80 to 0.89 for the ANN model, while the value of R2 ranges from 0.81 to 0.93 for GBR model. In this study, the GBR model outperforms the ANNs model based on its ensemble technique that uses multiple weak learners. As a result, the GBR model is more convincing in the prediction of syngas composition than the ANN model considered in this research.

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Questions to the Article: An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples (Preprint)

10.2196/preprints.26774 ◽

2020 ◽

Author(s):

Shu-Chun Kuo ◽

CHIEN WEI ◽

Willy Chou

Keyword(s):

Data Entry ◽

Ann Model ◽

Model Accuracy ◽

Data Format ◽

Raw Data ◽

Probability Of Survival ◽

Testing Data ◽

Laboratory Results ◽

Data Yield ◽

Artificial Neural Network Ann

UNSTRUCTURED The recent article published on December 23 27 in 2020 is well-written and of interest, but remains several questions that are required for clarifications, including (1) 30 feature variables with normalized format(mean=0 and SD=1) required to compare model accuracy with those with the raw-data format; (2)inconsistency in variable numbers between entry and preview panels in Figure 4 and reference typos; and (3) data-entry format with raw blood laboratory results in Figure 4 inconsistent with the model designed using normalized data to estimate parameters. We conducted a study using the training and testing data provided by the previous study. An artificial neural network(ANN) model was performed to estimate parameters and compare the model accuracy with those eight models provided by the previous study. We found that (1) normalized data yield higher accuracy than that with the raw data; (2) typos definitely exist at the bottom review (=32>30 variables in the entry) panels in Figure 4 and typos in Table 6; and (3)the ANN earns a probability of survival(=0.91) higher than that(=0.71) in the previous study using the similar entry data when the raw data are assumed in the app. We also demonstrated an author-made app using the visualization to display the prediction result, which is novel and innovative to make the result improved with a dashboard in comparison with the previous study.

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Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18105110 ◽

2021 ◽

Vol 18 (10) ◽

pp. 5110

Author(s):

Shu-Farn Tey ◽

Chung-Feng Liu ◽

Tsair-Wei Chien ◽

Chin-Wei Hsu ◽

Kun-Chen Chan ◽

...

Keyword(s):

Neural Network ◽

Early Stage ◽

Characteristic Curve ◽

Patient Discharge ◽

Ann Model ◽

Healthcare Settings ◽

High Area ◽

Icd 10 ◽

Artificial Neural ◽

Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

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Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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Intelligent long-term performance analysis in power electronics systems

Scientific Reports ◽

10.1038/s41598-021-87165-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Saeed Peyghami ◽

Tomislav Dragicevic ◽

Frede Blaabjerg

Keyword(s):

Performance Indicator ◽

Thermal Characteristics ◽

Operating Conditions ◽

System Level ◽

Power Electronic ◽

Reliability Modeling ◽

Artificial Neural Network Ann ◽

Long Term Performance ◽

Term Performance

AbstractThis paper proposes a long-term performance indicator for power electronic converters based on their reliability. The converter reliability is represented by the proposed constant lifetime curves, which have been developed using Artificial Neural Network (ANN) under different operating conditions. Unlike the state-of-the-art theoretical reliability modeling approaches, which employ detailed electro-thermal characteristics and lifetime models of converter components, the proposed method provides a nonparametric surrogate model of the converter based on limited non-linear data from theoretical reliability analysis. The proposed approach can quickly predict the converter lifetime under given operating conditions without a further need for extended, time-consuming electro-thermal analysis. Moreover, the proposed lifetime curves can present the long-term performance of converters facilitating optimal system-level design for reliability, reliable operation and maintenance planning in power electronic systems. Numerical case studies evaluate the effectiveness of the proposed reliability modeling approach.

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