Linköping Electronic Conference Proceedings
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Resource Simulator – a Tool for Scenario Studies on Limited Resources
Get PDFIn this paper, global resources have been gathered from different sources. From these resources scenarios have been made to get an overview of what resources will last at the present annual usage, as well as if we assume all individual would utilize the same amount or the difference between regions and populations. The most critical metal is according to this Zn, while also Cu, U, Co and Mn are relatively limited with reserves lasting in the range of 100 years. Some elements like P is not limited as such, but there is always a trade-off between total amount and at what concentration the extraction is made. For biomass and food we have enough resources if used efficiently. Wind, sun and hydropower are in reality unlimited resources for electricity production. We also have huge amounts of biomass. A question is what biomass should be used for. First it can be as a building material, then for chemical production and paper/packaging and last as energy source seems reasonable
Modelling a Cement Precalciner by Machine Learning Methods
Get PDFThis work is a feasibility study of modelling the calcination process in a cement precalciner by employing machine learning algorithms. Calcination plays a significant role in characterising the clinker quality, energy demand and CO2 emissions in a cement production facility. Due to the complex nature of the calcination process, it has always been a challenge to reasonably model the precalciner system. This study is an attempt of finding a feasible alternative to answering this challenge. In this study, six machine learning algorithms were tested to analyse three output variables, which are, 1). the apparent degree of calcination, 2). CO2 molar fraction (dry basis) and 3).water molar fraction in the precalciner outlet stream. Fifteen input variables were used to train the algorithms, of which the values were obtained through a large number of simulated datasets by applying mass and energy balance to the precalciner system. A number of machine learning algorithms showed better predictability and Artificial neural network (ANN) showed the best performance for all three output variables
Comparison of Absorption and Adsorption Processes for CO2 Dehydration
Get PDFCaptured carbon dioxide (CO2) must be dehydrated prior to transport or storage because of possibilities for corrosion and hydrate formation. CO2 dehydration can be performed by absorption, typically into triethylene glycol (TEG) followed by desorption or by adsorption on a solid (typically a molecular sieve) followed by desorption. In this work, the process simulation program Aspen HYSYS is used to calculate material and heat balances for a TEG based absorption process and a molecular sieve adsorption process to achieve less than 30 ppm water in the dehydrated gas. The absorption and stripping columns were modelled using a specified Murphree stage efficiency on each absorption and stripping stage. In the base case, the absorption and adsorption pressure was 40 bar and the inlet temperature was 30 °C. An additional stripping column was added below the desorption column to obtain a low water content. In the molecular sieve based process, all the process units except the adsorption/stripping units were simulated in Aspen HYSYS. It is simulated reasonable process alternatives for CO2 dehydration down to water levels of 30 and 5 ppm. The simulations combined with cost estimation indicate that a TEG based process is the most economic process both for dehydration down to 30 ppm and to 5 ppm water in dehydrated gas
Anaerobic Digestion of Aqueous Pyrolysis Liquid in ADM1
Get PDFAqueous pyrolysis liquid (APL) is formed from pyrolysis of lignocellulosic biomass and is considered as a possible feed for anaerobic digestion (AD). APL is known to contain many components that can have a negative impact on the AD process. In this study, APL is fed into experimental AD batch reactors and modelled as a substrate using the Anaerobic Digestion Model No. 1 (ADM1), extended by addition of the inhibitors phenol, furfural, and 5-hydroxymethylfurfural (HMF). Simulation performed with the extended ADM1 has a better ability to predict the behavior of APL than the standard ADM1. Reducing the inhibition constants and startup concentration of active biomass during simulation of APL at high organic load resulted in improved fit with experimental results, but these inhibitors alone cannot explain the reduced methane production rate at high organic load
Epidemiological Models and Process Engineering
Get PDFThe paper discusses the principles behind epidemiology models, with examples taken from the classic SIR (suspectible-infected-recovered) and SEIR (S-exposed-IR) models. Both continuous time deterministic and stochastic models are treated, where the stochastic models are based on Poisson-distributed events/reactions. These models use real approximations to the integers representing the number of people in each of the classes S, (E,) I, R. An alternative stochastic representation is the first reaction time description, where the variables are kept as integers, and where one instead computes the time between each event. The models are presented in a form compatible with standard chemical engineering models. Based on the model description, the SIR and SEIR models are fitted to a measles case study using the Markov Chain Monte Carlo approach. For the given data, the SIR model appears to give much smaller uncertainty in predicitons. The continuous time stochastic description and the firt reaction time approaches give similar variation in the models. An important measure of the state of epidemics is the reproduction number, R, which tells whether the infection is growing or decreasing from an initial infection. The development of an expression for Ris indicated both from eigenvalues and from the Next-Generation Approach, and it is shown that the expression for R is identical for the SIR and the SEIR model. The principles of epidemiology model development discussed in the paper are used in models ranging from HIV/AIDS to COVID-19
Developing Voltage Droop/Compensation Controller for a Hydro Power Controller in Modelica
Get PDFWith the introduction of unregulated renewable energy such as wind, solar and tidal power, the operation of the electrical grid has become more and more challenging. The more dynamic production pattern requires more advanced control algorithms in order to maintain an acceptable voltage quality which is within the limits given by the electrical network regulators. Better tooling and improved simulation of different operation scenarios is required.
This paper presents the development of voltage droop/compensation controller as used in a typical hydro power controller. The controllers has been implemented using the Modelica language and are according to the Norwegian Energy Regulatory Authority (NERA). Having the controller available in Modelica makes it possible to integrate them with hydro power system models build with the use of OpenHPL. The behaviour of the controller have been tested against a verified generator model of the OpenIPSL
Simulation of the Effect of Local Electric Potential and Substrate Concentration on CO2 Reduction via Microbial Electrosynthesis
Get PDFIntegrating anaerobic digestion into electrochemical reactors is an advanced technology for biomethane recovery. Imposing low electric potential between electrodes, supplies CO2, electrons, and hydronium ions from anodic oxidation of organic and/or inorganic compounds. Then, autotrophic methanogens on the cathode produce methane from CO2 and H+ by electron uptake from the cathode. However, in mixed microbial environments, acetogens produce acetate as well. These reactions can take place via two different mechanisms, DIET (direct interspecies electron transfer) or IMET (indirect mediated electron transfer). This work investigates CO2 conversion to acetate and methane in an electrochemical biofilm reactor comparing the efficiency of CO2 reduction via DIET and IMET mechanisms at hydrogen evolving potentials from -0.3 to -0.7 vs SHE. The other goal is to prove the importance of mass balance in CO2 reduction at applied voltages. Simulations are done in AQUASIM version 2.1. Simulation results depicted that higher H+ concentration at -0.7 V vs SHE can reduce more CO2 in DIET with less current generation compared to IMET. This shows DIET the more efficient mechanism. Methane production is dominant in IMET model, however higher current is needed for CO2 fixation in this mechanism. Also, biomass concentration, acetate and methane production, substrate concentration, biofilm thickness, biomass distribution in biofilm, and current density over time in both mechanisms are investigated at variant voltages and substrate concentrations. Simulations showed that at high CO2 levels in both mechanisms CO2 conversion cannot reach maximum if the voltage is not high enough to supply H+
Effect of temperatures on anaerobic granulated biofilm modelling
Get PDFAnaerobic granulated biomass-based treatment is a sustainable alternative for municipal wastewater treatment. Each granule in the system is comprised of a complex community of anaerobic microorganisms embedded in a biofilm matrix. The aim of this work was to implement a biofilm model for simulation of biogas production and COD removal as observed in an experimental up-flow anaerobic sludge blanket (UASB) reactor system. Additionally, selected scenario simulations were carried out to assess the effect of temperatures (25, 16, and 12 °C) on granulated anaerobic reactor performance at different organic loading rates. The two main model components used are: Dynamic biochemical and physicochemical conversion processes (Anaerobic Digestion Model No. 1) and diffusive mass transfer within the granule (biofilm). The model was implemented in AQUASIM 2.1. Simulations gave insight into non-observables, especially intragranular biomass distribution and substrate profiles, which help our understanding of granule formation and evolution. Results reflected observed effluent COD concentrations and methane production rates at variable temperatures and reactor loadings. Simulations also confirmed observed steady-state reductions in COD removal efficiencies and methane fraction in biogas at increasing organic loading rate. Model simulations also showed intra-granular alkaline pH depth profiles with increasing organic loading rate which may explain calcium-based mineral core formation. The biomass composition and active regions in granules were not significantly affected by organic loading rate. At steady state, organic substrates especially monosaccharides and volatile fatty acids were predicted to degrade approximately within the outer 100 μm. In general, the model can be used as a tool to predict and simulate anaerobic granulated biofilm system performances in UASB reactor
Uncertainty quantification and sensitivity analysis during the development and validation of numerical artery models
Get PDFIncreasing age and cardiovascular diseases lead to stiffening of the vasculature. Knowledge about an individual’s arterial stiffness gives insights into the current state of the cardiovascular system and it is considered to be a valuable diagnostic index. However, arterial stiffness cannot be measured directly. Numerical modelling based on measurements of flow and deformation in an individual’s artery enable an indirect means. Our research aims to develop a method to estimate the local arterial stiffness of an artery from non-invasive measurements through inverse modelling. Experimental measurement limitations and the unmeasurable nature of model input parameters lead to uncertainties in the model prediction. Uncertainty quantification and sensitivity analysis (UQSA) inform about how the model prediction is influenced by these uncertainties. Due to the computational expenses of 3D fluid-structure interaction (FSI) models, we reduced the model’s complexity to a 1D model. To verify the 3D-FSI implementation and validate the 1D implementation we performed simulated inflation tests and compared the results with analytical theory. 3D-FSI simulations were performed and compared to the 1D-model predictions for different simplification assumptions. To quantify the impact of uncertainties in input data, polynomial chaos expansion for UQSA was applied to the 1D-model. This analysis revealed the model input parameters which lead to the highest variability in model prediction. UQSA showed that variations in the Young’s modulus and the lumen radius lead to the largest variability in the 1D-model prediction. Thus, we focused in the validation process on the comparison between the the arterial wall behaviour between the 1D and the 3D-FSI model
Self-imperative Care of Pregnancy using IoT Solutions
Get PDFTypically, routine prenatal care includes several in-person visits with healthcare professionals by pregnant women, where fetal and maternal assessments are performed. This paper proposes an architectural framework for prenatal care using non-invasive, simple, and low-cost internet of things (IoT) monitoring system. The aim is to design an IoT-based architecture that serves as a fundamental system for self-imperative care in regular pregnancy check-ups in the comfort of the home that offers routine prenatal screening tests. The system provides easy access to care regardless of the location and internet availability. We implemented preliminary architecture with simulated sensor data for blood pressure monitoring