Records Added in 2021
Records added in 2021
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Showing records 1 to 25 of 798. [First] Page: 1 2 3 4 5 Last
A Fructan Sucrase Secreted Extracellular and Purified in One-Step by Gram-Positive Enhancer Matrix Particles
Jingyue Wang, Huazhi Xiao, Fangkun Zhao, Bo Zhao, Min Xu, Zhijiang Zhou, Ye Han
December 6, 2021 (v1)
Subject: Biosystems
Keywords: Bacillus subtilis, Escherichia coli, fructan sucrase, secretory expression
Fructan sucrase is a kind of biological enzyme that catalyzes the synthesis of fructan, and fructan is a polysaccharide product with important industrial application value. In this study, the Fructan sucrase gene of Bacillus subtilis was cloned to plasmid PET-28A-ACMA-Z, and three clones were obtained after the transformation of Escherichia coli BL21, namely BS-FF, BSO, and BS. The clones BS-FF and BSO secreted the recombinant enzymes outside the cells, while the clone BS expressed them inside the cells. The induction experiment results showed that the optimum IPTG concentration in the medium was 0.5 mM and 1.0 mM for clones BS-FF and BSO, respectively, while the incubation conditions were at 28 °C for 8 h. The recombinant fructan sucrase was purified one step using a material called GEM particles. The results indicated that 95.25% of fructan sucrase expressed by the clone BS-FF could be secreted into the extracellular area, and even 98.78% by the clone BSO. With the above purification... [more]
The Effect of Carbonyl and Hydroxyl Compounds on Swelling Factor, Interfacial Tension, and Viscosity in CO2 Injection: A Case Study on Aromatic Oils
Asep Kurnia Permadi, Egi Adrian Pratama, Andri Luthfi Lukman Hakim, Doddy Abdassah
December 6, 2021 (v1)
Subject: Materials
Keywords: Carbon Dioxide, IFT, MMP, swelling factor, VIPS, viscosity
A factor influencing the effectiveness of CO2 injection is miscibility. Besides the miscible injection, CO2 may also contribute to oil recovery improvement by immiscible injection through modifying several properties such as oil swelling, viscosity reduction, and the lowering of interfacial tension (IFT). Moreover, CO2 immiscible injection performance is also expected to be improved by adding some solvent. However, there are a lack of studies identifying the roles of solvent in assisting CO2 injection through observing those properties simultaneously. This paper explains the effects of CO2−carbonyl and CO2−hydroxyl compounds mixture injection on those properties, and also the minimum miscibility pressure (MMP) experimentally by using VIPS (refers to viscosity, interfacial tension, pressure−volume, and swelling) apparatus, which has a capability of measuring those properties simultaneously within a closed system. Higher swelling factor, lower viscosity, IFT and MMP are observed from a C... [more]
Optimal Cleaning Cycle Scheduling under Uncertain Conditions: A Flexibility Analysis on Heat Exchanger Fouling
Alessandro Di Pretoro, Francesco D’Iglio, Flavio Manenti
December 6, 2021 (v1)
Keywords: flexibility, fouling, heat exchanger, maintenance, Scheduling
Fouling is a substantial economic, energy, and safety issue for all the process industry applications, heat transfer units in particular. Although this phenomenon can be mitigated, it cannot be avoided and proper cleaning cycle scheduling is the best way to deal with it. After thorough literature research about the most reliable fouling model description, cleaning procedures have been optimized by minimizing the Time Average Losses (TAL) under nominal operating conditions according to the well-established procedure. For this purpose, different cleaning actions, namely chemical and mechanical, have been accounted for. However, this procedure is strictly related to nominal operating conditions therefore perturbations, when present, could considerably compromise the process profitability due to unexpected shutdown or extraordinary maintenance operations. After a preliminary sensitivity analysis, the uncertain variables and the corresponding disturbance likelihood were estimated. Hence, cl... [more]
A Process for the Synthesis and Use of Highly Aromatic Organosilanes as Additives for Poly(Vinyl Chloride) Films
Safaa H. Mohamed, Ayad S. Hameed, Gamal A. El-Hiti, Dina S. Ahmed, Mohammed Kadhom, Mohammed A. Baashen, Muna Bufaroosha, Ahmed A. Ahmed, Emad Yousif
December 6, 2021 (v1)
Keywords: dehydrochlorination, organosilanes, photostability, Schiff’s bases, ultraviolet irradiation absorbers, weight and molecular weight loss
Three organosilanes were synthesized in good yields from the condensation of 4,4′,4″-((phenylsilanetriyl)tris(oxy))tribenzaldehyde and 4-substituted anilines under acidic conditions. The structure of the organosilanes was confirmed using a variety of techniques. Organosilanes were mixed with poly(vinyl chloride) (PVC) and homogenous films were produced. The effect of long-term irradiation on the films containing organosilanes was tested using various methods. Monitoring the infrared spectra of PVC films before, during and after irradiation processes showed the formation of side products comprising polyene, carbonyl and hydroxyl groups. The intensities of absorption bands due to these functional groups were much lower in the presence of organosilanes as compared to the blank film. Also, the decrease in the weight and molecular weight of PVC films after irradiation was lower in the presence of organosilanes. Additionally, there was a minimal surface change of irradiated PVC in the presen... [more]
Rethinking Computing Education with Vocareum and Canvas
Alexander Dowling
November 18, 2021 (v4)
Subject: Education
Keywords: Canvas, Colab, computer, data science, education, Jupyter, Learning Management System, Python, statistics, Vocareum
Presentation of Prof. Alexander Dowling's experience integrating Jupyter notebooks and computing into classes at the University of Notre Dame. Presented to ND faculty.
A 2-stage Approach to Parameter Estimation of Differential Equations using Neural ODEs
William Bradley, Fani Boukouvala
November 7, 2021 (v1)
Keywords: Neural ODEs, Neural-Networks, Nonlinear programming, parameter estimation
Modeling physio-chemical relationships using dynamic data is a common task in fields throughout science and engineering. A common step in developing generalizable, mechanistic models is to fit unmeasured parameters to measured data. However, fitting differential equation-based models can be computation intensive and uncertain due to the presence of nonlinearity, noise, and sparsity in the data, which in turn causes convergence to local minima and divergence issues. This work proposes a merger of Machine Learning (ML) and mechanistic approaches by employing ML models as a means to fit nonlinear mechanistic ODEs. Using a two-stage indirect approach, Neural ODEs are used to estimate state derivatives, which are then used to estimate the parameters of a more interpretable mechanistic ODE model. In addition to its computational efficiency, the proposed method demonstrates the ability of Neural ODEs to better estimate derivative information than interpolating methods based on algebraic... [more]
Perspectives on the Integration between First-Principles and Data-Driven Modeling
William Bradley, Jinhyeun Kim, Zachary Kilwein, Logan Blakely, Michael Eydenberg, Jordan Jalvin, Carl Laird, Fani Boukouvala
November 7, 2021 (v1)
Keywords: gaussian process regression, hybrid modeling, Machine Learning, model calibration, neural networks, physics-informed machine learning
Efficiently embedding and/or integrating mechanistic information within data-driven models is essentially the only approach to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive c... [more]
Supplemental Data for “Process Design and Techno-Economic Analysis of Biomass Pyrolysis By-Product Utilization in the Ontario and Aichi Steel Industries”
Jamie Rose, Thomas A. Adams II
November 5, 2021 (v1)
This is supplemental data for a paper submitted to the PSE 2021+ conference. It includes values used to calculate emissions reductions and financial value of biomass pyrolysis by-product utilization.
Valorization of Biomass Pyrolysis By-Products for Heat Production in the Ontario Steel Industry: A Techno-Economic Analysis
Jamie Rose, Thomas A. Adams II
November 5, 2021 (v1)
As part of efforts to reduce carbon emissions in the iron and steel industry, which are especially pertinent in Canada due to rising carbon taxes, Canadian producers have been investigating the effects of replacing coal used in pulverized coal injection with biochar. Although there has been research into the economic value and effect on net life cycle emissions of using the biochar product itself, there are no comprehensive techno-economic analyses which investigate the value and potential uses of the by-products of biomass pyrolysis. These by-products include volatile organic compounds, known collectively as tar or bio-oil, and light gases, which are mainly hydrogen, carbon monoxide, carbon dioxide, and methane. Since only 20-30% of the mass of pyrolyzed biomass is actually converted to char, with the rest converted to the by-products, [1] usage of these by-products is likely the key to increasing the value of biochar to a degree that makes up for the market price of biochar currently... [more]
Optimization under uncertainty of a hybrid waste tire and natural gas feedstock flexible polygeneration system using a decomposition algorithm
Avinash Subramanian, Rohit Kannan, Flemming Holtorf, Thomas A. Adams II, Truls Gundersen, Paul I. Barton
April 5, 2022 (v2)
Keywords: Decomposition Algorithm, Optimization under uncertainty, Polygeneration system, Stochastic Programming, Waste Tire, Waste-to-Energy
Market uncertainties motivate the development of flexible polygeneration systems that are able to adjust operating conditions to favor production of the most profitable product portfolio. However, this operational flexibility comes at the cost of higher capital expenditure. A scenario-based two-stage stochastic nonconvex Mixed-Integer Nonlinear Programming (MINLP) approach lends itself naturally to optimizing these trade-offs. This work studies the optimal design and operation under uncertainty of a hybrid feedstock flexible polygeneration system producing electricity, methanol, dimethyl ether, olefins or liquefied (synthetic) natural gas. The recently developed GOSSIP software framework is used for modeling the optimization problem as well as its efficient solution using the Nonconvex Generalized Benders Decomposition (NGBD) algorithm. Two different cases are studied: The first uses estimates of the means and variances of the uncertain parameters from historical data, whereas the seco... [more]
Set Membership Estimation for Dynamic Flux Balance Models
Xin Shen, Hector Budman
October 21, 2021 (v1)
Subject: Biosystems
Keywords: dynamic flux balance model, multiparametric programming, multiplicity, set membership estimation, variable structure system, weighted primal dual method
A set membership estimator (SME) based on limited number of measurements is proposed for estimating metabolite concentrations using dynamic flux balance models (DFBMs). To deal with multiplicity of solutions of the DFBM, a weighted primal dual method is used to find solutions that best fit the data. Multiparametric nonlinear programming is applied to propagate uncertainty in initial concentrations along a batch/fed-batch operation. The proposed method has been applied to E. coli batch and fed-batch fermentation without noise.
Towards the Development of a Diagnostic Test for Autism Spectrum Disorder: Big Data Meets Metabolomics
Juergen Hahn
October 21, 2021 (v1)
Subject: Biosystems
Keywords: autism spectrum disorder, fisher discriminant analysis, kernel partial least squares
Autism Spectrum Disorders (ASD) are a group of neurological disorders that present with limited social communication/interaction and restricted, repetitive behaviors/interests. The current estimate is that approximately 1.9% of children in the US are diagnosed with ASD. While this is a high prevalence and the economic burden by ASD is significant, there is still considerable debate regarding the underlying pathophysiology of ASD. Because of this lack of biological knowledge, autism diagnoses are restricted to observational behavioral and psychometric tools. This work takes a step towards the goal of incorporating bio-chemical data into ASD diagnosis by analyzing measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways. Unlike traditional approaches that are based upon comparing differences in individual metabolite concentrations between children with and without an ASD diagnosis, we made use of multivariate classification via... [more]
Dynamic Modelling of T Cell Vaccination Response
Alisa Douglas, Thomas A Adams II, David A Christian
October 21, 2021 (v1)
Subject: Biosystems
Keywords: Dynamic Modelling, stochastic modelling, T cells, vaccine
In our previous work, a mathematical, agent-based dynamic model was developed which simulates the response of the mammalian omentum to a T cell vaccine injection during the expansion phase. The model tracks how each individual naïve T cell interacts with antigen presenting cells, and subsequently primes and divides over an 8-day period following vaccine injection. The model works from first principles; individual phenomena based on experimental observation and theory are incorporated into the model, and the collection of many such phenomena together create a nuanced model of the system as a whole. In this work, we show that the model works well in other relevant tissues, such as the spleen.
Ozone Sterilization of N95 Masks
Mohammad Irfan Malik, Karen Bechwaty, François Guitzhofer, Inès Esma Achouri
October 21, 2021 (v1)
Subject: Biosystems
Keywords: COVID-19, N95 mask, organic compounds, ozone disinfectant
The rapid spread of the COVID-19 worldwide pandemic at the beginning of 2020 has significantly affect-ed the global economy with severe human and economic losses. Despite the shortage of personal protective equipment, the facemask serves as a fundamental means to protect health care professionals' and re-strict the spread of the coronavirus. However, due to the limited stock of facemasks, many sterilization methods were developed to eliminate the infection and established strategies for fast and repeated reuse without affecting the filtration efficiency. The current study extrapolates the effective utilization of the ozonic sterilization of the N95 mask. First, we demonstrated the potential of ozone as a disinfectant that successfully destructs the organic food colour compounds deposited on the N95 mask; In the quantitative part of this research, the N95 facemask pieces were soaked in diphenylamine solution and later oxidized with ozone under the different intervals of time. Finally,... [more]
A Study of Factors Affecting Iron Uptake from a Functionalized Hibiscus Beverage
Ade O Oyewole, Levente L Diosady
October 21, 2021 (v1)
Keywords: Hibiscus sabdariffa, iron bioaccessibility, iron deficiency
Iron deficiency accounts for over 50% of the world’s anaemia burden and it is widely prevalent in low- and middle-income countries. In response to the menace of iron deficiency in Sub-Saharan Africa, a commonly consumed beverage, the vibrantly red aqueous extract of the calyces of Hibiscus sabdariffa, has been functionalized. To determine the conditions that could potentially result in the most iron uptake by consumers of the functional beverage, the present study evaluated the effect of the factors that could influence the bioaccessibility of its iron content in the gastrointestinal (GI) tract.

Hibiscus beverage was fortified to contain, 6 mg iron per 250 mL of the beverage, by adding O.358 mM solution of ferrous sulphate salt to top up the native iron content determined to be 0.93±0.19 mg/ 250 mL. Also, a competing chelating agent - disodium EDTA was added to increase the bioaccessibility of iron from the beverage. Previous results showed the feasibility of releasing iron from th... [more]
Lessons Learned from Three Decades of Global Automation Experience Across Five Industries
Lane Desborough
October 21, 2021 (v1)
Keywords: artificial pancreas, automated insulin delivery, diabetes
My current work on Automated Insulin Delivery (the so-called “artificial pancreas”) directly benefits from two decades of experience gained implementing and remotely monitoring automation in complex and challenging industrial cyberphysical systems all over the world; systems upon which society depends. This talk will cover topics including experimentation, modeling, simulation, and outcome measure sample statistics, as well as controller design considerations including human factors, objective functions, and final control element challenges.
Detection and Diagnosis of Ring Formation in Rotary Lime Kilns
Lee D Rippon, Barry Hirtz, Carl Sheehan, Travis Reinheimer, Cilius van der Merwe, Philip Loewen, Bhushan Gopaluni
October 21, 2021 (v1)
Keywords: data visualization, Fault Detection, fault diagnosis, process monitoring, pulp and paper, rotary kiln
Rotary lime kilns are large-scale, energy-intensive unit operations that serve critical functions in a variety of industrial processes including cement production, pyrometallurgy, and kraft pulping. As massive expensive vessels that operate at high temperatures it is imperative from economic, environmental, and safety perspectives to optimize preventative maintenance and production efficiency. To achieve these objectives rotary kilns are increasingly outfitted with more sophisticated sensing technology that can provide additional operating insights. Although increasingly intricate data is collected from industrial operations the extent to which value is extracted from this data is often far from optimal. Our research aims to improve this situation by developing data analytics methods that leverage advanced industrial sensor data to address outstanding process faults. Specifically, this research investigates the use of infrared thermal cameras to detect and diagnose ring formation in r... [more]
Constant Power Generation by Scheduling Installation of SOFC Modules Operating in Varying Power Mode
Mina Naeini, Thomas A Adams, James S Cotton
October 21, 2021 (v1)
Keywords: constant power output, optimal operating conditions, optimal operating mode, performance degradation, Solid Oxide Fuel Cells
In this paper, producing constant power load of 550 MW from systems of Solid Oxide Fuel Cells (SOFCs) operating in varying power output mode was investigated. This is useful because previous research has shown that individual cells can have significant lifetime extensions when operated according to certain dynamic trajectories in which power production decreases over time. In this study, we determined that a constant net power output of a system comprised of many individual SOFC modules can be achieved by scheduling the installation and operation of each SOFC module in a particular manner. All the modules were operated under the optimal operating conditions obtained in our previous optimization study where power output of each module declined over time. The dynamic degradation of SOFCs was taken into ac-count by using a detailed mathematical model of long-term performance degradation as a function of operating conditions. The result is a system in which every 5 days, one new SOFC modul... [more]
Bypass Control of HEN Under Uncertainty in Inlet Temperature of Hot Stream
Chaitanya Manchikatla, Zukui Li, Biao Huang
October 21, 2021 (v1)
Keywords: Affine Control Policy, Heat Exchanger Network, Model Predictive Control, Uncertain Optimization
The dynamic control of Heat Exchanger Network is significant for developing energy efficient and safe industrial processes. In this project, the hot stream's inlet temperature is considered uncertain because it is common in industries. The cold stream is bypassed around the heat exchanger. This project aims to track the setpoint temperature of the mixed stream by manipulating the bypass fraction of the cold stream around the Heat Exchanger given uncertainty in the inlet temperature of the hot stream. The control is implemented in Nonlinear Model Predictive Control (NMPC) framework. The uncertainty in the optimal control problem (OCP)is dealt by using scenario tree based approximation as well as affine policy based method. The model of the system considered is based on the first principles model, i.e. dynamic model of shell and tube heat exchanger. The Orthogonal collocation technique is used to discretize the first principles model into the system of algebraic equations. The results... [more]
Purification Methods for Captured CO2 from Petroleum Coke Oxy-Combustion Power Plants
Tia Ghantous, Ikenna J Okeke, Thomas A Adams II
October 21, 2021 (v2)
Keywords: Carbon Dioxide Capture, eco-technoeconomic analysis, oxy-combustion, Petroleum Coke
We present eco-technoeconomic analyses of four processes, including two novel designs, for the purification of captured CO2 from flue gas for a petroleum coke (petcoke) oxy-combustion power plant operated with carbon capture and sequestration (CCS). A base case petcoke oxy-combustion design obtained from a previous study consisting of flue gas water removal using condensation was used in this study. Other purification processes evaluated consist of a cryogenic distillation petcoke oxy-combustion with CCS, an oxygen deficient petcoke oxy-combustion with CCS and a catalytic dehydration petcoke oxy-combustion via hydrogen conversion with CCS. An eco-technoeconomic analysis considering greenhouse gas (GHG) emissions, levelized cost of electricity (LCOE), thermal efficiency and CO2 product purity to meet pipe-line specifications, was conducted on all purification candidates. This revealed that base case design did not meet the CO2 pipeline specifications. The highest LCOE was attributed to... [more]
Adaptive State Feedback Stabilization of Generalized Hamiltonian Systems with Unstructured Components
Seyedabbas Alavi, Nicolas Hudon
October 21, 2021 (v1)
Keywords: Adaptive stabilization, Hamiltonian system, Lyapunov stability, Parameter Estimation
This paper considers the problem of adaptive state feedback controller design for stabilizing the generalized Hamiltonian systems with unstructured components. This class of models enables one to exploit the dissipative-conservative structure of generalized Hamiltonian systems for feedback control design while relaxing the burden of deriving an exact structured model representation. First, an efficient adaptation law is designed such that a correct value of parameters is estimated. Assuming that the overall system is stabilizable, and under mild assumptions on the unstructured part of the dynamics, a stabilizing adaptive control law is designed to stabilize systems to the desired steady-state. The stability of the closed-loop system is demonstrated using Lyapunov stability arguments. A numerical illustration of the proposed approach is presented to demonstrate the potential of the design method.
CSChE Systems & Control Transactions Volume 1
Thomas A Adams II
October 21, 2021 (v2)
Subject: Uncategorized
Keywords: Canadian Chemical Engineering Conference 2021, Systems & Control Division Conference Proceedings
Selected Extended Abstracts from the
 Systems & Control Division Sessions of the 
71st Canadian Chemical Engineering Conference
, October 24-27, 2021, Montréal, Québec
Improving Transactional Data System Based on an Edge Computing−Blockchain−Machine Learning Integrated Framework
Zeinab Shahbazi, Yung-Cheol Byun
October 14, 2021 (v1)
Keywords: blockchain, edge computing, Industrial Internet of Things, Machine Learning, smart manufacturing
The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge compu... [more]
Formulation and Stability of Cellulose-Based Delivery Systems of Raspberry Phenolics
Josipa Vukoja, Ivana Buljeta, Anita Pichler, Josip Šimunović, Mirela Kopjar
October 14, 2021 (v1)
Keywords: anthocyanins, antioxidant activity, cellulose/raspberry encapsulates, inhibition of α-amylase, phenolics
Encapsulation of bioactives is a tool to prepare their suitable delivery systems and ensure their stability. For this purpose, cellulose was selected as carrier of raspberry juice phenolics and freeze-dried cellulose/raspberry encapsulates (C/R_Es) were formulated. Influence of cellulose amount (2.5%, 5%, 7.5% and 10%) and time (15 or 60 min) on the complexation of cellulose and raspberry juice was investigated. Obtained C/R_Es were evaluated for total phenolics, anthocyanins, antioxidant activity, inhibition of α-amylase and color. Additionally, encapsulation was confirmed by FTIR. Stability of C/R_Es was examined after 12 months of storage at room temperature. Increasing the amount of cellulose during formulation of C/R_E from 2.5% to 10%, resulted in the decrease of content of total phenolics and anthocyanins. Additionally, encapsulates formulated by 15 min of complexation had a higher amount of investigated compounds. This tendency was retained after storage. The highest antioxidan... [more]
Influence of Processing Parameters on Phenolic Compounds and Color of Cabernet Sauvignon Red Wine Concentrates Obtained by Reverse Osmosis and Nanofiltration
Ivana Ivić, Mirela Kopjar, Lidija Jakobek, Vladimir Jukić, Suzana Korbar, Barbara Marić, Josip Mesić, Anita Pichler
October 14, 2021 (v1)
Keywords: Cabernet Sauvignon concentrate, nanofiltration, phenolic compounds, reverse osmosis
In this study, Cabernet Sauvignon red wine was subjected to reverse osmosis and nanofiltration processes at four different pressures (25, 35, 45, and 55 bar) and two temperature regimes (with and without cooling). The aim was to obtain concentrates with a higher content of phenolic compounds and antioxidant activity and to determine the influence of two membrane types (Alfa Laval RO98pHt M20 for reverse osmosis and NF M20 for nanofiltration) and different operating conditions on phenolics retention. Total polyphenol, flavonoid, monomeric anthocyanin contents, and antioxidant activity were determined spectrophotometrically. Flavan-3-ols and phenolic acids were analyzed on a high-performance liquid chromatography system and sample colour was measured by chromometer. The results showed that the increase in applied pressure and decrease in retentate temperature were favorable for higher phenolics retention. Retention of individual compounds depended on their chemical structure, membrane pr... [more]
Showing records 1 to 25 of 798. [First] Page: 1 2 3 4 5 Last
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