Accelerating kinetic parameter identification by extracting information from transient data : a hydroprocessing study case

Hydroprocessing reactions require several days to reach steady-state, leading to long experimentation times for collecting sufficient data for kinetic modeling purposes. The information contained in the transient data during the evolution toward the steady-state is, at present, not used for kinetic modeling since the stabilization behavior is not well understood. The present work aims at accelerating kinetic model construction by employing these transient data, provided that the stabilization can be adequately accounted for. A comparison between the model obtained against the steady-state data and the one after accounting for the transient information was carried out. It was demonstrated that by accounting for the stabilization, combined with an experimental design algorithm, a more robust and faster manner was obtained to identify kinetic parameters, which saves time and cost. An application was presented in hydrodenitrogenation, but the proposed methodology can be extended to any hydroprocessing reaction

Predictive Modeling for an Industrial Naphtha Reforming Plant using Artificial Neural Network with Recurrent Layers

In this research, a layered-recurrent artificial neural network (ANN) using the back-propagation method was developed for simulation of a fixed-bed industrial catalytic reforming unit called Platformer. Ninety-seven data points were gathered from the industrial catalytic naphtha reforming plant during the complete life cycle of the catalytic bed (about 919 days). Ultimately, 80% of them were selected as past horizontal data sets, and the others were selected as future horizontal ones. After training, testing, and validating the model with past horizontal data, the developed network was applied to predict the volume flow rate and research octane number (RON) of the future horizontal data versus days on stream. Results show that the developed ANN was capable of predicting the volume flow rate and RON of the gasoline for the future horizontal data sets with AAD% (average absolute deviation) of 0.238% and 0.813%, respectively. Moreover, the AAD% of the predicted octane barrel levels against the actual values was 1.447%, which shows the excellent capability of the model to simulate the behavior of the target catalytic reforming plant

Developing an online predictor to predict product sulfur concentration for HDS unit

Hydrodesulfurization (HDS) is an important process in refining industries. Advanced control system (e.g. model predictive controller) requires on-line measurement of the product sulfur at the reactor outlet. However, most HDS processes do not have a sulfur analyzer at the reactor outlet. In order to predict product sulfur concentration usually a data based sulfur predictor is developed. Performance of data based predictor is usually poor since some of the input parameters (e.g. feed sulfur concentration) are unknown. The objective of this thesis is to overcome these limitations of data based predictors and develop an online product sulfur predictor for HDS unit. In this thesis, a hybrid model is proposed, developed and validated (using industrial data), which could predict product sulfur concentration for online HDS system. The proposed hybrid structure is a combination of a reaction kinetics based HDS reactor model and an empirical model based on support vector regression (SVR). The mechanistic model runs in off-line mode to estimate the feed sulfur concentration while the data based model uses the estimated feed sulfur concentration and other process variables to predict the product sulfur concentration. The predicted sulfur concentration can be compared with the lab measurements or sulfur analyzer located further downstream of the process at the tankage. In case there is a large discrepancy, the predictor goes to a calibration mode and uses the mechanistic model to re-estimate the feed sulfur concentration. The detailed logic for the online prediction is also developed. Finally a Matlab based Graphical User Interface (GUI) has been developed for the hybrid sulfur predictor for easy implementation to any HDS process

Developing an online predictor to predict product sulfur concentration for HDS unit

Hydrodesulfurization (HDS) is an important process in refining industries. Advanced control system (e.g. model predictive controller) requires on-line measurement of the product sulfur at the reactor outlet. However, most HDS processes do not have a sulfur analyzer at the reactor outlet. In order to predict product sulfur concentration usually a data based sulfur predictor is developed. Performance of data based predictor is usually poor since some of the input parameters (e.g. feed sulfur concentration) are unknown. The objective of this thesis is to overcome these limitations of data based predictors and develop an online product sulfur predictor for HDS unit. In this thesis, a hybrid model is proposed, developed and validated (using industrial data), which could predict product sulfur concentration for online HDS system. The proposed hybrid structure is a combination of a reaction kinetics based HDS reactor model and an empirical model based on support vector regression (SVR). The mechanistic model runs in off-line mode to estimate the feed sulfur concentration while the data based model uses the estimated feed sulfur concentration and other process variables to predict the product sulfur concentration. The predicted sulfur concentration can be compared with the lab measurements or sulfur analyzer located further downstream of the process at the tankage. In case there is a large discrepancy, the predictor goes to a calibration mode and uses the mechanistic model to re-estimate the feed sulfur concentration. The detailed logic for the online prediction is also developed. Finally a Matlab based Graphical User Interface (GUI) has been developed for the hybrid sulfur predictor for easy implementation to any HDS process

Unsupervised-Learning Assisted Artificial Neural Network for Optimization

Innovations in computer technology made way for Computational Fluid Dynamics (CFD) into engineering, which supported the development of new designs by reducing the cost and time by lowering the dependency on experimentation. There is a further need to make the process of development more efficient. One such technology is Artificial Intelligence. In this thesis, we explore the application of Artificial Intelligence (AI) in CFD and how it can improve the process of development. AI is used as a buzz word for the mechanism which can learn by itself and make the decision accordingly. Machine learning (ML) is a subset of AI which learns any method without the need for any explicit algorithm. Deep Learning is another subset of ML, which is different in its composition. Deep Learning, or Neural Networks (NN), is made up of nodes like the neurons and works on the principle of the human brain. NN can be exploited for any problem without the need for any explicit algorithm for the task. It can be achieved by analyzing and inferring from the observations. Artificial Neural Network (ANN) is used for data analysis and Convolutional Neural Networks (CNN) for image analysis. Our area of interest herein is ANN and its application for a medical equipment called Convective Polymerase Chain Reaction (cPCR) device. Many have relied on engineering experimentation to develop an optimized PCR device, which requires high cost and time. That makes the use of PCR devices less cost-effective as a commonplace for healthcare. We optimize a convective PCR reactor using a high-fidelity CFD-based surrogate model to find an economical and performance-effective one. We plan numerous possible design combinations, evaluating DNA doubling time. Based on these results, an accurate surrogate model is developed for optimization using Deep Learning. We produce two kinds of surrogate models using ANN; one by directly employing ANN and another by using unsupervised learning called, k-Means-Clustering-Assisted ANN, and then compare the results from these two methods. For developing a suitable model of ANN to fit our data, we carry out the analysis of model accuracy and obtain the best design by using a differential evolution method. The best designs obtained by the two methods are verified with the corresponding result obtained from CFD. This shows an effective way of designing an optimized device by reducing the number of CFD simulations required for the development. Consequently, the computational results demonstrate that the convective PCR device can be efficiently developed using our proposed methodology, making it viable for point-of-care applications

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PD

Koneoppimiskehys OPC UA datalle (Industry 4.0)

Machine learning has rapidly gained popularity in all industries with the increase of computational power and data gathering capabilities. Process industry is a good candidate for machine learning based modeling due to the large amounts of data gathered and need for accurate process state predictions. In this work the viability of combining the OPC UA protocol with existing open source machine learning libraries to create data driven models and generate real time predictions was studied. Scikit-learn was used to generate soft sensor style models for the butane content of a debutanizer column output. The data for offline model training was dynamically fetched from an OCP UA server and with a trained model predictions could be generated in real time. The accuracy of the generated models needs to be further researched with better methodology and larger datasets.Koneoppiminen on kasvattanut suosiotaan nopeasti kaikilla toimialoilla laskentatehon ja datankeruun kasvaessa. Prosessiteollisuus on hyvä kandidaatti koneoppimispohjaiselle mallinnukselle suurien datamäärien sekä vaadittujen tarkkojen prosessimallien takia. Tässä työssä tutkittiin mahdollisuutta OPC UA protokollan yhdistämistä olemassaolevien avoimen lähdekoodin koneoppimiskirjastojen kanssa mittausdataan perustuvien mallien opettamiseksi ja reaaliaikaisten ennusteiden luomiseksi. Scikit-learn kirjastoa käytettiin luomaan malleja butaaninpoistokolonnin ulostulon butaanipitoisuuden ennustamiseen. Data mallien offline opetukseen ladattiin dynaamisesti OPC UA palvelimelta ja valmiiksi opetetulla mallilla ennusteita voitiin generoida reaaliaikaisesti. Luotujen mallien tarkkuutta täytyy tutkia tarkemmin paremmalla metodologialla ja suuremmilla datamäärillä

Scanning Probe Microscopy Studies of Petroleum Chemistry: Substrate-Dependent Catalytic Properties of MoS2 and Automating Scanning Probe Microscopy with Machine Learning

With the growth of the population, society’s energy demands are mostly reliant on petroleum products that come from the refining of crude oil. Most of these refining reactions have been developed through averaging spectroscopic techniques, but scientists do not know exactly what is happening in these processes at the nano and atomic levels. This information is crucial when designing an efficient refining process that produces petroleum products that emit fewer harmful gases when combusting. Scanning probe microscopy techniques have become a powerful tool to look into the chemical structures found in petroleum products, to understand catalytic reactions in refining processes, and to find new non-combustible uses for these products. In this dissertation, I show how scanning probe microscopy (SPM) techniques, especially non-contact atomic force microscopy (NC-AFM) can provide an atomic-level understanding of the chemical structures and active catalytic sites that play a role in these refining processes. First, I studied hydrodesulfurization reactions that use molybdenum disulfide as a main catalyst to explore the effect of layer thickness, strain, and underlying substrates on its electronic and catalytic properties. Here, I present the first NC-AFM experiments investigating the active catalytic sites of molybdenum disulfide on industrially relevant substrates. Through these experiments, I found how NC-AFM techniques on insulators need to be improved to achieve high-resolution images that are comparable to those collected on metal substrates. Second, I created Auto-HR-AFM, a machine-learning script that collects optimal high-resolution NC-AFM images. Auto-HR-AFM is a modular and open-source script that provides an initial framework for a fully automated SPM. Expanding on this framework will widen the use of scanning probe microscopy techniques to non-experts and the automation will increase the time the system is kept running to collect large optimal datasets. Ultimately, these studies will broaden the use of high-resolution SPM techniques and help create more efficient catalysts and refining processes to produce cleaner and more efficient petroleum products

Forecasting net energy consumption of South Africa using artificial neural network

Abstract: This work proposes the use of Artificial Neural Network (ANN) as a new approach to determine the future level of energy consumption in South Africa. Particle Swarm Optimization (PSO) was used in order to train Artificial Neural Networks. The population size, the percentage losses, the Gross Domestic Product (GDP), the percentage growth forecasts, the expected Final Consumption Expenditure of Households (FCEH) as well as the relevant manufacturing and mining indexes are the “drivers” values used for the forecasts. Three growth scenarios have been considered for the forecasting namely low, moderate and high (less energy intensive) scenarios. These inputs values for the period of 2014 to 2050, from the Council for Scientific and Industrial Research (CSIR), were used to test data and validate the use of this new approach for the prediction of electricity demand. An estimate of the annual electricity demand forecasts per scenario was calculated. Besides the speed of the computation, the proposed ANN approach provides a relatively good prediction of the energy demand within acceptable errors. ANN was found to be flexible enough, as a modelling tool, showing a high degree of accuracy for the prediction of electricity demand. It is expected that this study will contribute meaningfully to the development of highly applicable productive planning for energy policies

Development of an ANN Model for RGB Color Classification using the Dataset Extracted from a Fabricated Colorimeter

  Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under detection is one of the results of the proposed classifier. The work demanded the collection of about 5000 color codes which in turn were subjected to algorithms for training and testing. The open-source platform TensorFlow for ML and the open-source neural network library Keras were used to construct the algorithm for the study. The results showed an acceptable efficiency of the built classifier represented by an accuracy of 90% which can be considered applicable, especially after some improvements in the future to makes it more effective as a trusted colorimeter.