ABSTRACT. In the AI era, where machine learning and advanced computational techniques are employed to optimize designs, assess risks, and predict performance of load-bearing structures under various conditions, the importance of simulation governance has never been more pronounced. This talk will discuss key frameworks that ensure the credibility of simulations, focusing on critical areas such as verification, validation, uncertainty quantification and data integrity. Applications in the design and analysis of civil engineering structures will be considered together with some sustainability discussions.

ABSTRACT. Being one of the most energy-intensive industries, mining accounts for more than one-third of industrial final energy consumption. With the growing mineral demand, combined with declining ore grades, it is expected that the energy demand in mining will only grow in the future, which will potentially increase its already large greenhouse gas footprint. With increasing energy cost, renewable energy presents a viable option not only to improve the environmental footprint, but also reduce overall costs with optimized operation of mines. While the renewable energy generators i.e., solar photovoltaics and wind turbines offer numerous benefits like modularity, environmentally friendliness, and natural availability; the major drawbacks are their temporal intermittency and seasonal and long-term variability. Hence, these generators pose a resource risk that the actual quantity of wind and solar irradiation can be less than expected. The resource risk imposes uncertainty in short-, medium- & long-term energy generation and consumption. Hence such risk needs to be actively considered and mitigated during evaluation and operational phase of renewable or hybrid energy system projects. This paper provides a comprehensive review of renewable resource risk quantification techniques. Subsequently, a matrix for renewable energy resource risk quantification methods are analyzed i.e., renewable reliability (i.e., the percentage of demand met by renewables), energy deficit and energy over supply index, probability of exceedance (PoE) for annual energy production (AEP), probability of generating at least k MW of renewable power, capacity factor. Finally, the proposed matrix is used to assess the effect of different risk mitigation options, e. g. the optimal size of energy storage. Some conclusions drawn from the work are, indeed, the renewable energy generation potential of mines will vary depending on onsite availability of renewable resources. Accordingly, the preferred share of different renewables sources, here solar and wind, in a mining energy grid will differ significantly at different sites. The reliability of renewable energy generation from the same solar-wind combination can be utterly different in different locations. Interestingly, the reliability trend can reverse for two different locations, meaning increasing share of solar in a wind-solar mix can result in both decreasing or increasing reliability based on the location. Additionally, using both solar and wind energy together, it is possible to reduce the impact of yearly variability of each source and have a more consistent supply of energy. For financial calculation, annual energy production with a probability of exceedance can act as a better indicator. When it comes to the battery energy storage sizing, the benefit diminishes with increasing size. Meaning, the reduction in overall energy deficit from a solar-wind-battery system decreases exponentially with increasing battery energy storage size. Additionally, the lowest possible energy deficit is also heavily dependent on the share of solar-wind in the system.

ABSTRACT. Life cycle analysis is considered as a valuable decision-making tool to oversee the environmental impact of a product through its different stages. Starting from the raw material sourcing up to the end-of-life processes of the product. Life cycle costing has been added to the life cycle analysis to augment the economic aspects. One of the main drawbacks of the life cycle analysis is the concern of single path for the life stages as it evaluates single option for each life stage and adds the impact to the following stages. in this study we present a tool to evaluate the environmental and economic impact of different options in life cycle stages and present the possible combination of different life stages options, calculating the resulted emissions, energy intensity and cost of each different combination scenario. The study takes wind turbine blade as a case study where two raw material options is taken in consideration glass fiber reinforced polymers and carbon fibers reinforced polymers combined with two supply options Europe and China market, four manufacturing site options (onsite, Denmark, Germany, and China) and four end of life processing options (reuse, incineration, landfill, and mechanical shredding). The results showed the different combinations scenarios emissions in the range of (74.4 – 16.5) tons of CO2 eq and the energy intensity between (261.2 and 862.7 GJ) and the cost vary from 89,158€ to 22,114 €. This work presented a method for mapping and evaluating environmental and economic sustainability of a wind turbine blade, through different life cycle pathway.

ABSTRACT. This paper focuses on the utilization of dynamic simulation models in the planning of experiments for control development. The simulation system is a set of models based on the first principles for system level simulation of the complete TCP-100 research facility at Plataforma Solar de Almería (CIEMAT). This new research facility replaced the 32-year-old ACUREX facility with which so many advances in Automatic Control were reached by the research community. The dynamic models are developed to speedup this research for the new field. The part for control development is the solar field whose parabolic trough collectors (PTCs) are modelled in module level and combined into PTCs and loops. are presented models of the parabolic trough field (PTC) will be validated with experimental data and the loops are controlled. The sequential loops have different operating conditions. This research uses the parameters based on the parameter selection from providers’ data sheets and the engineering design project of the TCP-100. All state variables are temperatures and input variables include solar radiation, ambient temperature and several setpoints. The system level model has been implemented in the Modelica language. All state variables are temperatures according to the modelling hypothesis applied. solar radiation, ambient temperature, setpoints for both circuits pumps, setpoints for two loops control valves and setpoint for air cooling power. The simulation experiments are first focused on the modules, PTCs and loops of the solar field and the full model need to be extended with dynamic LE models before going to the full simulation tests. In the test campaigns with the new facility, the dynamic LE models are used for planning the test cases.

ABSTRACT. This work is a step towards conceptualizing a smart multi-modular structure, whose main application is solar energy harvest, with the innovative idea of connectors that can be controlled to mitigate motions and loads in a changing environment. The paper presents selected preliminary results from experimental tests of an array of floating column-based modules exposed to regular waves of different periods. Each pair of neighboring modules was connected by two spring connectors with both tension and compression stiffness. The paper presents an investigation of motion responses versus load frequencies corresponding to four tested spring stiffnesses.

The model test results serve as a basis for validating a numerical model that is implemented for control design and simulation purposes. Wave-, mooring- and connector forces are considered in the simulations. The proposed method will act as a tool for further evaluation of the effect of changing the connection stiffness according to the incoming waves and the investigation of whether it is beneficial to apply a smart connector that can adapt to varying sea states.

ABSTRACT. To address climate change and energy security issues from fossil fuels, wind power is a promising renewable energy source, projected to grow significantly by 2050. Offshore wind energy, especially floating offshore wind farms shows great potential due to higher and more consistent wind speeds at sea. However, these turbines have negative environmental burdens throughout their life cycle. This The present study focuses on a comprehensive cradle-to-grave life cycle assessment of the Hywind Tampen floating offshore wind farm in Norway. The assessment covers all stages from manufacturing, transportation, installation, operation, and maintenance to decommissioning, utilizing openLCA® software and ecoinvent 3.9 database with the ReCiPe 2016 impact assessment method. Key findings indicate that manufacturing is the primary contributor to total emissions, followed by operation and maintenance. The study emphasizes the necessity of developing more sustainable manufacturing methods, designing turbines that are more efficient and versatile, and better maintenance forecasting and planning in order to minimize the environmental impact of these turbines.

ABSTRACT. This study presents a techno-economic assessment of an amine-based carbon capture technology. The aim is to compare different methods to evaluate the cost effect of doubling the capacity. A base case was established in Aspen HYSYS with 15 m absorber packing height, 6 m desorber packing height, removal efficiency of 85 % and a heat exchanger minimum temperature approach (ΔTmin) of 10 °C. In a first additional case the flue gas flow rate was doubled and in the second case a new absorber in parallel was added. Then dimensioning and cost estimation was carried out using Aspen HYSYS spreadsheets to automatically calculate CAPEX and OPEX and total carbon capture cost. To estimate the Bare Erected Cost (BEC), the Enhanced Detailed Factor (EDF) and the Aspen Process Economic Analyzer (APEA) were employed. The EDF method determines the installation cost of each piece of equipment, while the Nazir-Amini method only offers the TPC without calculating individual equipment. Applying the EDF method, the TPC (CAPEX) for the base case, the doubled feed gas case and two-absorber case were calculated to 76, 141 and 150 MEuro respectively. This illustrates that cost increase may be less than proportional to the flow rate increase. The estimated annual OPEX for the base case is about 42.5 MEuro, while for the two alternatives the OPEX was very close to the double of the base case. The estimated carbon capture costs for the base case, two-absorber case, and double feed gas scenario were 52.4 €/ton, 51.8 €/ton, and 50.5 €/ton, respectively. The study demonstrates that a combination of Aspen HYSYS simulation, Aspen Process Economic Analyzer and the EDF method is an effective method to evaluate different alternatives for increasing the capacity.

ABSTRACT. Carbon capture and utilization (CCU) is a growing field in chemical engineering with high expectations to replace fossil carbon. This paper focuses on modeling and simulation of a CCU process chain utilizing biogenic CO2. A scenario with a pulp mill recovery boiler effluent is assumed. CO2 capture is performed with a membrane-based system. This is followed by methanol synthesis, and the majority of produced methanol is directed to dimethyl carbonate (DMC) synthesis.

The process chain with fixed process design was simulated for different scenarios of the flue gas properties. The key process indicators were observed. Further, the flexibility of the processes was evaluated to mitigate the changes in process indicators due to fluctuating flue gas properties. Finally, model parameter uncertainties and modeling assumptions were discussed. The results indicate the level of uncertainties of CCU models and their key process indicators that should be considered when moving on to the system level simulations and techno-economic or life cycle analyses.

ABSTRACT. The amine-based CO2 capture rig at USN in Porsgrunn has been operating since 2007. The models fitted to experimental data have given poor predictive properties for conditions outside the experimental range. In this study, the main aim was to develop predictive models in Aspen HYSYS and Aspen Plus for the CO2 test rig. The models’ accuracy was verified by comparing different test scenarios with results from the models. Aspen HYSYS and Aspen Plus have simulated eleven scenarios (test series) with varying process parameters. In Aspen Plus two approaches were used, fitting either the interfacial area or the holdup factor to minimize the deviation. The Aspen HYSYS model with Murphree efficiencies from top to bottom (0.11, 0.1, 0.09, and 0.07) predicted seven scenarios with an average deviation of 12-24 % when changing MEA flowrate and CO2 concentration. An Aspen HYSYS model could not predict all experimental data when varying MEA temperature, inlet gas temperature and flow rates. A fitted rate-based model in Aspen Plus gave a more minor error than the equilibrium-based Aspen HYSYS model. In the rate-based model with interfacial area fitted, most of the scenarios can be predicted by a model with correlation Brf-85 (mass transfer), Brf-85 (heat transfer) and an interfacial factor of 0.5. Minimum and maximum deviations for different scenarios were 2.1 and 9 %. In the approach with fitting of the holdup factor, the Brf-92 holdup method with a holdup factor of 0.5 gave the best fit, resulting in an average deviation of 1.4-9 % from the test results across all scenarios.

ABSTRACT. In this study, a fluidized bed electrified calciner was designed, and various operating conditions were investigated by CPFD simulations. It was found that maintaining a constant fluidization velocity while increasing the temperature of hot cylinders or preheating the raw meal significantly enhances the calcination degree. Altering the fluidization velocity while keeping temperatures constant also greatly affects the calcination degree and particle entrainment. A fluidization velocity of 0.3 m/s appears to be optimal for the reactor, whereas 0.8 m/s resulted in complete entrainment of the bed. The maximum calcination degree achieved was 90% with preheated meal. The average meal residence time was found to be 24-26 s.

ABSTRACT. In addition to solidification, steelmaking slags may undergo phase transformations in solid state during their cooling process. The mineralogy of these oxide slags is significantly influenced by the chemical composition and cooling rate. For the phases forming, two distinct solidification modes can be assumed, depending on the cooling rate: equilibrium cooling and Scheil-Gulliver cooling. Characterization methods, such as scanning electron microscopy (SEM) and electron probe microanalyzer (EPMA) allow analyzing the elemental composition of individual phases.

Here, computational thermodynamics were applied in phase identification of crystallized electric arc furnace (EAF) slags. FactSage 8.3 thermodynamic calculation software was used to estimate the composition of stable phases as a function of temperature. Solid solutions with varying compositions were considered in this study. The calculation results from two solidification modes, i.e., equilibrium cooling and Scheil-Gulliver cooling, were saved in Excel spreadsheets. A MATLAB script was developed to go through the results and find the phase with a composition closest to the input values. For both solidification modes, the composition and temperature best fitting the input analysis was determined. The input is the elemental composition of the phase of interest, acquired using EPMA. After the data processing, the results are visualized in graphs, illustrating the analyzed and estimated compositions of the identified solid solution phase and its occurrence temperature.

ABSTRACT. Non-metallic inclusions (NMIs) are micrometer-sized particles observed in all steel materials, often considered detrimental. In this study, NMIs in titanium-deoxidized steels were investigated, complemented with thermodynamic assessment for more accurate phase characterization. The NMIs were analyzed with a Jeol JSM-7900F FESEM-EDS (Field Emission Scanning Electron Microscope equipped with Energy Dispersive X-ray Spectroscope). For automated particle analyses on FESEM, Aztec Feature runs were carried out on polished steel samples, providing the elemental composition, in addition to morphological data, for each observed NMI.

Utilizing the obtained EDS analyses, the fractions of oxides (Al2O3, MnO, TiOx), manganese sulfide (MnS), and titanium nitride (TiN) in each NMI are estimated with a MATLAB script. Based on the estimated phase contents, a composition-based classification method for the NMIs is presented. To visualize the phase contents of the observed NMIs, the calculated compositions are plotted on MnO-TiO2-Ti2O3 ternary diagrams.

ABSTRACT. Understanding the steel microstructure formation during thermal treatments is crucial for controlling the mechanical properties of a steel product. One of the important factors affecting the subsequent microstructure development is the austenite grain size. To gain understanding of the effect of temperature dependent nucleation and growth rates, as well as providing the tools for quantitatively control the austenite grain size distribution, we have implemented a cellular automata (CA) model for describing austenite nucleation and growth during heating, as well as austenite grain growth during holding in temperatures above the austenitization temperature. The model implementation is based on previous study of Sieradzki and Madej for grain growth during recrystallization now augmetned with the relevant equations for describing the austenite nucleation and growth. The model parameters and their effect on austenite grain size distributions are tested with numerical experiments. The developed computational tool will serve as a basis that can be parameterized with experimental data in the future, which will then enable quantitative predictions for austenite phase transformation and grain size development.

ABSTRACT. To connect the molecular length scale phenomena to the macroscopic length scale in diffusion controlled growth in solid state, there is need to consider the movement of individual atoms in the crystal lattice and examine the length scale effect where the average density of the atoms approaches to the continuum macro scale. For this purpose a lattice random walk model has been constructed to represent the diffusion of atoms to form a precipitate. Once the atom is in contact with the precipitate surface, the precipitate grows and the atom is not anymore contributing to the random walk. Through the model, it is possible to evaluate the concentration fluctuations at different length scales in diffusion controlled growth and connect the continuum description of diffusion to the atomic level description. In the current study, two-dimensional lattice random walks and growth are considered. The study contributes to the modelling efforts of understanding diffusion controlled precipitate growth in steels

ABSTRACT. Metastable austenite significantly impacts the mechanical properties of Advanced High-Strength Steels (AHSS), especially Medium Mn Steel (MMnS), where its formation rate during intercritical annealing depends strongly on the initial microstructure. This study employs thermodynamic and diffusion-controlled simulations to investigate the formation of two distinct morphologies of retained austenite–lamellar and blocky known also as blocky– commonly observed in an intercritically annealed hot-rolled MMnS. Utilizing Thermo-Calc software, coupled with its DIffusion-Controlled TRAnsformation module (DICTRA), phase equilibria are computed, and one-dimensional diffusion equations are solved. Characterization of the microstructure of a medium manganese steel (MMnS) with a nominal composition of Fe-0.4C-6Mn-2Al-1Si-0.05Nb (in wt. %), hot rolled and intercritically annealed for 1 hour at 680°C, was performed using Energy Dispersive Spectroscopy coupled with Transmission Electron Microscopy (EDS-TEM) and Transmission Kikuchi Diffraction (TKD). These techniques were used for experimental validation and verification of the simulations. Simulations explore the competition between cementite and austenite growth. Specifically, the growth of austenite starting on various interphase boundaries was examined using spherical and planar geometries. This approach resulted in the formation of blocky and lamellar austenite morphologies, respectively. The findings indicate that austenite first nucleates at the BCC/BCC interface and transforms 40% of the BCC phase within 1 second at 680°C. Cementite then starts to form, limiting further austenite transformation. Finally, cementite particles continue to grow to a size of about 100 nm. These simulation results align well with experimental findings.

ABSTRACT. Energy storage can be charged when energy is cheap and discharged when it is expensive to make an energy system more profitable or used to make the plant operation more efficient to reduce $CO_2$ emissions. To optimize long term energy storage with conventional methods a long time horizon must be used. When the long term energy storage is combined with a complex energy system the computational cost becomes large when using conventional methods. To reduce the time horizon, an algorithm will be used to decide the state of charge of the long term energy storage at the end of the day. This algorithm is trained using machine learning with data of the optimal state of charge obtained by running computationally heavy long time mixed integer linear programming ahead of time. Then a one-day or week mixed integer linear programming optimization will be done for the production planning. The seasonal patterns of the long term energy storage can then be captured while giving the plant operator a simple one-day or week production plan. A case study will be done with a combined heat and power plant system with 4 boilers, a long-term thermal storage, and a hydrogen storage system. Using this method the complexities of a multi energy system with long term energy storage can be captured while doing day ahead production planning.

ABSTRACT. Batteries are used in electric vehicles as well as for stationary applications. In the first case we usually want a high energy density as kWh/kg, while stationary applications are less sensitive to the energy density. Principally it may be a good idea to first use batteries for transportation applications and then when capacity has reached a certain level start using them for other applications in a “second life”. Both for optimizing the performance of operations in 1st and 2nd life as well as for making fair commercial agreements when selling used batteries for 2nd life applications, we need to make prediction of remaining useful life (RUL) as well as SOH (State of Health). For this purpose battery models are needed. In the paper we show a methodology for building useful battery models built on own experiments as well as literature data. Single cells of NMC (Li-NiMnCo-batteries) as well as LFP (Li-ionphosphate batteries) have been cycled as well as cells in series. EIS, Electrochemical Impedance spectra as well as dQ/dV has been measured for each cycle. These data then have been used for development of SOH and RUL models using different regression methods. The models are described, discussed and results shown in the paper.

ABSTRACT. The importance of EVs and li-ion batteries are pinpointed in the automotive industry during the last decade by increased growth of electrified powertrain. Li-ion batteries offer significant improvements in terms of energy and power density; however, safety challenges continue to exist. Different thermal, mechanical, or electrical abuse conditions in li-ion batteries can trigger a series of exothermic chain reactions in the battery cells and finally result in thermal runaway (TR) and combustion of battery cells and EVs. Furthermore, different battery technologies exploit various cell chemistries, leading to the distinct thermal behavior of battery cells during normal and abuse conditions. This work aims at investigating the TR kinetic mechanisms to evaluate thermal behavior of the battery cells under thermal abuse conditions. Furthermore, this work investigates the different li-ion battery cathode, anode and electrolyte materials to assess the safety of battery systems in EV application. The results revealed that unlike batteries with LiCoO2 cathodes with temperature threshold of 150 ℃, Li1.1(Ni1/3Co1/3Mn1/3)0.9O2 batteries do not undergo TR process at temperatures below 170 ℃. Moreover, the temperature peak is more hazardous in LiCoO2 batteries with LiPF6/PC: DMC electrolyte compared to the same battery with standard electrolyte. In addition, batteries with Lithiated Li4Ti5O12 anode showed safer TR process compared to all the previous battery types.

ABSTRACT. The share of renewable energy sources is rising, more specifically the amount of wind power in Sweden. The intermittent character of wind power results in volatile power production. Therefore, wind farm owners experience difficulties in robust production planning. This research investigates the effects of grid-scale battery storage integrated with wind energy on system resilience, in the context of technical performance and economic viability. A microgrid model is developed in Modelon Impact to simulate and optimize the technical performance. The model consists of a wind turbine, battery storage, electricity grid, and load demand.

To assess technical performance, a planned production profile is constructed based on the average wind speed over 12-hour intervals over a week. The study examines how accurately the actual wind farm production and the planned production profile align. It is found that integrating battery storage reduces electricity required for grid balance by 8%, 11%, and 18% for battery capacities of 1 MW, 2 MW, and 5 MW, respectively.

Furthermore, a techno-economic analysis is conducted to evaluate the Net Present Value of the system. Results indicate a negative result for both the wind turbine and battery components, mainly due to low revenue generation from the wind turbine and high investment costs associated with battery deployment. Optimization of the system is achieved through the interior point method, focusing on minimizing battery controls and operational costs. This made the battery operation more smoothly, however at the cost of more electricity being required for grid balance.

Integrating wind power with grid-scale batteries strengthens energy systems and ensures resilience for the future.

ABSTRACT. In offshore oil and gas production gas turbines are used for both power production and to provide process heat. CO2 emissions from the gas turbines accounts for about 25% of the total Norwegian emissions, and installing a bottoming cycle to produce power by recovering heat from the gas turbine exhaust is one way to reduce these missions. When installing a steam bottoming cycle offshore, the total weight and size will be important, and there is need for a compact heat recovery steam generator (HRSG). A compact HRSG will often need to be designed with smaller tube diameters than conventional on-shore steam generators. To increase confidence in the compact design, the heat transfer and pressure loss models need to be accurate for the relevant geometry ranges. In this work, a compact steam generator is designed using optimisation procedures where the total weight of the steam generator has been minimized for a desired duty with restrictions for pressure losses. A range of correlations from literature were used for calculation of the performance. The results from the optimisation show that the ’heaviest’ results was about three times the minimum weight than the ’lightest’. To increase confidence in the results, and to provide a recommendation for design models, a validated CFD model was used to perform a numerical analysis of the optimized geometry and comparing this with the optimization results.

ABSTRACT. Oil and gas will remain an important source of energy for years and it is crucial to improve oil recovery with less carbon footprint to meet the future energy demands. Carbon capture utilization and storage offers a potential solution to mitigate the effects of anthropogenic CO2 and to reduce the direct CO2 emissions from stationary sources into the atmosphere. The captured CO2 can be utilized to enhanced oil recovery (EOR) and is injected into the depleted oil fields or saline aquifers, or into the oil fields for storage and/or EOR. However, the injected CO2 can be reproduced without contributing to EOR. This is due to the breakthrough of CO2 into the well. Also, the corrosive mixture of CO2 and water can be produced from the production well. This may cause damages to the pipeline and process equipment on the platform. Autonomous inflow control valves (AICVs) can mitigate these problems. They may reduce or stop the reproduction of CO2 from the zones with CO2 breakthrough and reduce the production of mixture of CO2 and water. The main objective of this study is modelling and simulation of oil production in a heterogenous reservoir using CO2-EOR in combination with AICVs. The simulation models are developed using an industry standard software. The outcome of numerical simulations is analyzed to study the effect of various parameters on oil recovery. In addition, the impact of AICVs on EOR is assessed against perforated casing completion (without AICV). The results demonstrate that oil recovery factor, water cut, and cumulative gas production are better in the wells completed with AICVs than perforated casing completion. This will result into both increased oil production and a better CO2 storage potential.

ABSTRACT. Injection of CO2 for Enhanced Oil Recovery (CO2-EOR) is used in fields with high amount of residual oil. CO2-EOR refers to a technology where supercritical CO2 is injected into an oil reservoir to increase the well performance. CO2 reduces the viscosity and increases the mobility of the oil, and thus it is possible to produce from the oil reserves that would otherwise be inaccessible. CO2-EOR in combination with CO2-storage is an attractive method to increase the oil production from mature oilfields, and at the same time reduce the carbon footprint from industrial sources. Utilizing autonomous inflow control valves (AICVs) in CO2-EOR projects contributes to a better distribution of CO2 in the reservoir, reduction in production of water and CO2 mixture, and thereby increased storage capacity of CO2. The main objective of this study is modelling and simulation of oil production from an oil reservoir using CO2 water alternating gas (CO2 WAG) injection in combination with advanced wells that are completed with AICVs. Furthermore, performance evaluation of the AICV technology and sensitivity analysis of parameters affecting the WAG process are completed. The miscible CO2-WAG with advanced wells model was developed using the commercial software Computer Modelling Group (CMG). The results from the simulations indicate that well completion with AICV can maintain good oil production while the production of water is decreased from 3e+06 m3 to 9.8e+04 m3 which corresponds to 97% reduction in water production. This is beneficial as the possibility of corrosive mixture production is avoided. The sensitivity analysis of the simulation results affirms that permeability, well placement, and well spacing have impact on productivity in terms of both oil recovery and water production in the WAG EOR method. The results indicate that permeability increase has a slight increment effect on oil recovery. The well spacing analysis shows that increasing the distance between the wells will increase the oil recovery and delay the water breakthrough. Lastly the well placement analysis shows that vertical injection of miscible CO2 produces more oil than horizontal injection of miscible CO2. AICVs restrict the production of mixture of CO2 and water, and thereby cause a better distribution of CO2 in the reservoir.

ABSTRACT. Carbon capture utilization and storage (CCUS) offers a potential solution to mitigate the effects of anthropogenic CO2 and to reduce the direct CO2 emissions from stationary sources into the atmosphere. The captured CO2 is injected into deep saline-water saturated formations or in depleted oil and gas fields, or into the oil fields for storage and/or enhanced oil recovery (EOR). The primary objective of this study is to identify and analyze the critical parameters affecting CO2 plume development in the reservoir. Understanding the subsurface dynamics of carbon sequestration will facilitate to plan the subsurface process better. The simulation models are developed using the commercial software Computer Modelling Group, CMG. The plume dynamics that include plume volume and plume geometry over 30 years of injection and 170 years of post-injection period is investigated. Additionally, the contribution of different trapping mechanisms over the time horizon in the storage process is assessed. Moreover, a sensitivity analysis is done for evaluating the impact of variables including porosity, permeability, injection rate, and injector bottom hole pressure. The simulation results show that CO2 plume propagates at an increased rate during the injection period and continues to disperse at a comparatively reduced rate after the injection ends. The horizontal spread of plume is significantly greater than the vertical propagation when the horizontal permeability is larger than the vertical. Additionally, the plume volume shows a linear relationship with the injected CO2 amount. In terms of storage efficiency, the most prevalent CO2 is free phase super critical CO2 that contributes around 80% of the stored CO2 whereas the rest are structurally or residually trapped and dissolved CO2. From the sensitivity analysis in a homogenous reservoir, it can be concluded that the horizontal permeability is impacting the most (42%) for structural and residual trapping of CO2 whereas porosity impacts the most (38%) for dissolution of CO2 contributing to solubility trapping mechanism.

ABSTRACT. To optimise the methanation of synthesis gas (syngas) with a focus on achieving maximum methane and minimum CO, a comprehensive thermodynamics analysis of CO2 hydrogenation is conducted. This study will help us to understand the thermodynamic behaviour of the reactions involved in methanation process. We have investigated the species, CO2, H2, CH4, H2O, and CO at the equilibrium in a temperature range of 200-1200°C with pressure variation of 1 atm to 300 atm and a fuel composition of H2/CO2 ratio of 2 to 6. Low temperatures (200-400°C) and high pressures are favourable for the consumption of CO2 and H2 as well as to obtain maximum CH4. Also, in this temperature range, there is no carbon formation. Higher H2/CO2 ratio favours CO2 consumption but influence CH4 formation. Trace amount of O2 in syngas is unfavourable for methanation and additional CH4 is favourable. The carbon formation is a serious issue that starts around 400°C and above, for all the fuel compositions at 1 atm. This can be shifted to slightly higher temperatures, i.e., 600°C if high pressures are selected. The study can help us to select the optimum conditions (temperature, pressure, and H2/CO2 ratio) to perform the experiments to achieve maximum CH4 by full methanation of CO2 avoiding the carbon formation issue. This will also support us for the development of catalysts and processes for the production of natural gas which can be reintegrated into the network of natural gas.

ABSTRACT. The amount of CO2 in the atmosphere is continuously increasing, resulting in climate change and global warming. Industrial processes contribute a substantial share in the amount of CO2 released to the atmosphere. On the other hand, different types of wastes and by-products are being produced by different industries which are deemed pollutants and require energy and capital to be safely managed through a circular economy perspective. A solution to simultaneously tackle both the CO2 emission and waste pollution problems would be of high value. CO2 sequestration by mineralization of CaO-rich industrial wastes is one potential solution. In such a process, CO2 reacts with the CaO in the waste and CaCO3 is produced. This product is thermodynamically stable and has multiple uses. Many studies in the literature have reported use of various CaO-rich wastes to capture CO2, but they are mostly based on lab-scale experiments, and mostly the focus is on the chemistry of the suggested processes. Hence, there is a need to study the technical and economic feasibility of up-scaled industrial versions of such processes. In this study, four different aqueous indirect mineralization processes applying different chemicals, all with a relatively high performance documented from laboratory experiments, are scaled up to industrial size with a CO2 capturing capacity of 400 t/y using an in-house-made process simulation tool. Furthermore, an economic analysis and environmental assessment are conducted for all processes, and the results are compared. Finally, parameters impacting the techno-economic feasibility of each process are evaluated through a sensitivity study. The results indicate that the potential of capturing CO2 and producing CaCO3 can be as high as 530 kg and 1200 kg per ton of the waste while the yearly energy consumption can be as low as 0.7 kWh per kilogram of captured CO2. The aqueous indirect mineralization of CO2 can be profitable and the emitted CO2 by the process can be so low as 6% of the captured amount.

ABSTRACT. Stainless steelmaking slags are, currently, one of the most common non-utilized slags in steelmaking. Concerning that the slag obtained from a submerged arc furnace utilized in ferrochrome production, this means not only losing iron to the slag but also valuable chrome. Hence, recovery of iron and chrome has not only a business incentive but also an important function for green industry initiative by reducing the requirement of virgin material. However, one of the challenges of slag recycling can be the energy-intensive nature of such practice. Therefore, an energy efficient approach in material recovery could enhance the incentive of recycling of slag instead of the current practice of land field storage; one such approach is mixing the solid ferrochrome slag to liquid slag from the steelmaking production line. To that end, a static model of a suspended slag particle inside a melt has been developed to investigate the effect of particle size on evolution of temperature within the solid particles. The simulation showed that changes in the diameter of particle can have a significant effect on energy diffusion from the melt into the slag particle. As an example, the simulation suggests that the temperature magnitude at the centre of a 2mm-in-diameter particle reaches 1200°C after 1s simulation time while, with 5mm particles the temperature magnitude is less than 200 °C. This behaviour is amplified further when the diameter of particle increases further showing a delaying behaviour of particle’s diameter on energy diffusion and, consequently, remelting of solid particles.

ABSTRACT. Iron ore pellet reduction in shaft furnaces represents a critical process in the steelmaking industry, with energy consumption being a key factor influencing both economic viability and environmental sustainability. This study employs HSC Chemistry software to model and simulate the energy consumption of hydrogen reduction of iron ore pellets under varying water vapor content within the shaft furnace. Thermodynamic modeling was carried out as the first step to analyze the effect of water vapor on the thermodynamic equilibrium, determining the possible range of water vapor content. Subsequently, energy consumption of the process was modeled based on heat and mass balance. Through comprehensive analysis, we investigate the impact of water vapor on the reduction kinetics and overall energy efficiency of the process. Our findings reveal significant insights into optimizing energy consumption and operational parameters to enhance the sustainability and cost-effectiveness of iron ore pellet reduction. This research contributes to the ongoing efforts towards achieving greater efficiency and reduced environmental footprint in the steelmaking industry.

ABSTRACT. Oxyfuel combustion compliments decarbonization efforts by reducing the energy needs in high temperature industries. Steel reheating furnaces are good candidates for full oxyfuel operation since it can lead up to 30% energy savings. Linde uses an in-house tool to simulate reheating furnaces for air-fuel to oxyfuel conversion. This paper follows a real customer case starting with an airfuel simulation setup used to analyze the furnace followed by oxyfuel simulations for burner design and energy savings estimations. These simulations lead to a successful installation of oxyfuel burners for the reheating furnace located at Ovako Imatra site. After the commissioning is completed, performance evaluation is done by comparing a reference airfuel operation period with an oxyfuel combustion period. Full oxyfuel conversion results in 27% energy savings for hot charge and high production rate periods thanks to significantly lower flue gas losses. Removal of nitrogen from the oxidizer decreases the flue gas volume, hence reduces the total heat capacity of the off-gas stream. The savings are estimated as 30% for cold charge and average production rate periods.

ABSTRACT. Medium manganese steels (MMnS) are known as third-generation high-strength steels, providing an excellent balance of high strength and ductility at a lower cost than the second-generation. This study explores the computational design of MMnS with a better combination of strength, ductility, and hydrogen embrittlement resistance. Mechanical properties vary mainly due to changes in chemical composition and processing routes. Computational approaches enable precise optimization of these parameters, avoiding the inefficiencies of traditional trial-and-error. The increasing demand for steels more resistant to hydrogen embrittlement highlights the need for the effective development of new alloys. Therefore, CALPHAD-based thermodynamic calculations using ThermoCalc (TCFE13, MOBFE 2) and JMatPro 12.1 software were employed to design a novel MMnS chemistry. The optimised chemical composition was determined to be (wt.%): 0.35C, 9Mn, 1Si, 1Mo, 1&3 Al, 0.05& 0.1Nb, and 0.3V. ThermoCalc precipitation simulations identified 0.1% Nb as optimal since higher Nb contents reduce carbon in austenite, lowering its stability and decreasing molybdenum carbide fractions. This Nb content results in NbC formation with an average size distribution around 3.4 nm and increases the yield strength by 143 MPa. Additionally, 3%Al enhanced the volume of NbC and the yield strength. In comparison, 1%Mo optimizes the mean size and volume fraction of NbC, strengthening the alloy and serving as an effective hydrogen trapping site. JMatPro's thermo-physical simulation demonstrated that at a low annealing temperature of 500°C, austenite fractions at 1% and 3% Al are 12% and 16%, respectively, which increase to 59% and 91% by increasing the annealing temperature to 700°C.

ABSTRACT. This paper presents simulations of an ammonia cracker process using Aspen HYSYS. Ammonia is identified as both a promising low-emission maritime fuel and an energy carrier. This study focuses on converting ammonia to hydrogen through an ammonia cracker process. In the literature, there are found simulations of similar processes, but not much about optimization of the ammonia cracker process. A centralized ammonia cracking process was designed using the Peng-Robinson fluid package and Gibbs reactor in Aspen HYSYS. Gibbs reactors were employed to simulate both the cracker and the furnace (ammonia combustion reaction). Simplified assumptions included using a 100 % efficient splitter instead of a pressure swing adsorber. The ammonia feed had a molar flow rate of 500 kmole/h. The simulations included a base case scenario and an improved case for energy optimization. The base case scenario resulted in a total production of 0.13 kg of hydrogen per kg of ammonia feed. The improved case resulted in a production of 0.14 kg hydrogen. This was due to using the energy content present in the hydrogen and nitrogen product streams for warming up the ammonia before entering the cracker. This work demonstrates that Aspen HYSYS is a useful tool for optimizing the energy efficiency of an ammonia cracker process. 

ABSTRACT. To reduce emissions and save fuel in offshore power production using gas turbines, one can use the gas turbine exhaust as a heat source for a bottoming cycle for heat and power production. This can replace about one in four gas turbines. In offshore applications weight and size become more important and thus a once-through steam generator (OTSG) is a way to achieve low weight for the bottoming cycle. To reduce the size and weight of the OTSG further, one can reduce the tube diameter in the tube bundles. In this work a three-core OTSG, representing the economizer, evaporator, and superheater, was modelled and the design optimized to achieve minimum weight, while producing a certain amount of power and keeping within constraints of flue gas and steam pressure losses. This was done for varying tube diameters in each of the cores, in steady state. Afterwards transient simulations were performed for each optimized design to find their response times to a step change in the gas turbine load. The evaporator has the biggest impact on both the weight and the response time, while the superheater and economizer had similar and smaller impacts on both the weight and response time.

ABSTRACT. Lime production is integral to the chemical recovery cycle in chemical pulping mills and traditionally relies on fuel combustion with resulting CO2-emissions. While Nordic pulp mills predominantly use carbon-neutral biofuels such as tall oil pitch or bark, concerns over future biomass scarcity prompt more sustainable biomass management needs and therefore the exploration of alternative lime calcination methods. Electrification is a promising option for supplying the necessary heating demand for lime calcination as CO2-emissions are ultimately subject to the carbon intensity of the electricity grid which is increasingly relying on renewable or low-carbon sources. From an economic perspective, chemical pulp mills are given the opportunity to become biorefineries by shifting towards electrified solutions and a producer of higher-value biofuels for a potentially tighter market. Plasma calcination, in specific, offers several advantages over conventionally used lime kilns such as significantly faster reaction times and reduced reactor volume. The scope of this work includes the development of mathematical models for conventional lime kilns and plasma calcination to conclusively perform techno-economic assessments of both technologies and identify decarbonization potentials for lime calcination in the pulp and paper industry. Energetic requirements for both technologies under the investigation of different fuels for the lime kiln are analyzed with corresponding CO2-emissions and an economic analysis including projected fuel and electricity prices as well as revenues from excess heat and avoided biomass usage. A sensitivity analysis is further conducted to identify key influential parameters in the developed models. Initial model results showcase that resulting specific CO2-emissions from plasma calcination are highly dependent on the carbon intensity of the electricity grid and are lower in several regions in the world in comparison to lime kiln emissions. Potential obstacles remain regarding the significant electricity consumption of the plasma generator and the corresponding load that is being put on the electricity grid.

ABSTRACT. Accurate measurement of flow rate of the multiphase flow of oil, gas and water from the oil wells, is an important part of the oil and gas industry. This enables the safe operation and proper optimization of the production. With the increasing availability of process data, machine learning algorithms are used to create models for various applications. The application of these algorithms for flow rate estimation provides a more accurate representation of the oil and gas production process. In this paper, two oil wells and ten machine learning algorithms are evaluated. Long short-term memory (LSTM) provides the best results with Mean absolute percentage error of 1.96% for Well 1 and 1.56% for Well 2. In addition, the effects of noise on the models are explored. Median filter with window size of three provides good noise reduction. The uncertainty of the predictions are quantified using 95% confidence intervals in XGBoost model.

ABSTRACT. The aim of this article is to develop and compare machine learning (ML) methods with activated sludge models (ASM) for estimation of effluent nutrients in the Hias Process. The Hias Process is a novel moving bed bioreactor with enhanced biological phosphorus removal and simultaneous nitrification and denitrification (MBBR-EBPR-SND). As the main energy cost of the nutrient removal process is aeration, it is necessary to design of energy-efficient control strategies that ensure compliance with legal requirements for nutrient removal in real-time while optimizing the aeration rates. The first step in control strategy design is development of models that represent the main process dynamics. The case study data set of four months was collected from a 192 000 PE municipal MBBR process at Hias water resource recovery facility in Norway. The Hias Process consists of three anaerobic and seven aerobic zones, where biomass carriers flow continuously submersed in the used water and remove over 90% of the phosphorus. The online measurements include used water flowrate, aeration rates, dissolved oxygen, suspended solids, and soluble nutrients PO4, COD, NO2 and NO3. Reduced ASM model, support vector regression (SVR) and long short-term memory neural network (LSTM), with and without dynamic time-delay, were developed to predict the effluent PO4 in the Hias process. The model prediction accuracies were compared using correlation coefficients and trend figures. The SVR model with fine gaussian kernel gave best results with strong R index of 0.9. The LSTM model reached a sufficient R index of 0.6 and the reduced ASM2d model a weak R index of 0.2. Including the dynamic time-delay improved the model accuracy. The machine learning models with dynamic time-delay will be developed further for energy-efficient control strategy development.

ABSTRACT. Pyrolysis of biosolids aims to reduce solid volumes and improve energy recovery; however, the pyrolysis liquid (PL) is a by-product that has no good application. One idea is to link pyrolysis and anaerobic digestion (AD), in which PL can be valorized for methane production. PL contains various compounds that potentially threaten the stability of AD. This study, therefore, aims to extend the current Anaerobic Digestion Model No.1 (ADM1) and evaluate the influence of phenol, furfural, 5-hydroxymethylfurfural (5-HMF), styrene, and ammonia from PL on AD. Two lab-scale AD reactors were simulated and compared with experimental data: one fed with hydrolyzed sludge and the other fed with an additional stream of PL. The simulation accurately predicts hydrolyzed sludge as substrate, while the simulation of the reactor co-digesting hydrolyzed sludge and PL overestimates methane production. Ammonia, phenol, and styrene were identified as the most significant inhibitors. However, based on the overestimation of methane production, it is clear that the PL has more inhibitors present than those implemented in the model. Simulations further showed that an additional stream of PL increased methane production by 4.3%, even with significant inhibition by the compounds.

ABSTRACT. Achieving “Climate-Neutral and Smart Cities” is now very high on the agenda and the city of Oslo has set an even more ambitious goal of becoming a zero-emission city. However, the promotion of more compact development may lead to some negative effects such as the entrapment of polluted air, wind tunnel effects or urban heat islands. Green infrastructure (GI) can be used as a mitigation measure, bringing many benefits such as improving air quality, regulating thermal environment, reducing energy consumption, managing storm water, or promoting urban biodiversity. In this work, we aim to map the existing green roof infrastructure in Oslo and develop an evidence-base strategy for its further development, and enhance the understanding and supplement the existing policies developed by the local authorities. Interviews with stakeholders revealed the practical challenges such as structural limitations, high installation and maintenance costs, and regulatory compliance issues. However, they also recognized the significant environmental advantages that highlight the importance of green roofs in urban sustainability strategies. Geographical information system (GIS) tools are used to identify the potential areas for further green roof implementation, taking into account the spatial, morphological and environmental conditions. 91 Priority green roof areas (PRIOGRAs) and 13 Potential green roof areas (PGRAs) in Oslo are identified as the most suitable for green roof installations after applying filters like roof surface area greater than 250 m2, and dominating roof area and slope criteria, exclusion of cultural heritage buildings and existing green roofs, tree density per person deficit, and building age. 2044 roofs can be considered as suitable without the criteria of building age. These findings will potentially help providing actionable insights for policymakers, urban planners, and the research community.

ABSTRACT. The treatment unit process in textile wastewater is complex due to the influx of pollutants and changes in operational factors, which in turn need optimized operation to stabilize the system. However, measuring the performance of the plant is not a once-off activity but rather an integrated simulation of the plant under operating scenarios. Thus, in this study GPS-X was used to model, simulate, and optimize the operational process control parameters to enhance the treatment plant pollutant removal efficiency under different scenarios. Wastewater samples were collected from the inlet and outlet of the treatment plant for six months and were analyzed in the laboratory for the selected dominant parameters, used for model calibration and validation. The sensitivity analysis was performed to identify the model variables and increase the quality of calibration. The results in existing model (Scenario I) compared to modified model (scenario II) for total suspended solids, chemical oxygen demand, total phosphorous, and NO3, were violated the compliance limit and the final effluent quality was categorized as poor. However, the energy consumption and operation cost for scenario II were reduced by 52.9 % and 56.98 %, respectively. Thus, GPS-X model predicted better for the modified process flow than scenario I.

ABSTRACT. Microbial biofilms matrix offer numerous benefits in bioprocessing and are crucial in various industrial and remediation processes. They facilitate electron exchange from solid surfaces when they interact with the environment. Emerging technologies such as biofilm-containing trickle bed reactors (TBR) and bioelectrochemical systems (BESs) for carbon dioxide (CO2) utilization, mostly rely on microbial biofilms matrix. Metabolic modeling of biofilm-based reactors enables detailed analysis of CO2 reduction within microorganisms, enhancing reactor efficiency. This study employs simulation models to analyze biomethanesynthesis within TBR and BES systems. AQUASIM simulation tool was used for conducting the simulation. Parameters such as non-stoichiometric ratio of substrates (CO2/H2, 1:1.33), stoichiometric ratio (CO2/H2, 1:4), hydraulic retention time (HRT) for 1,10 and 20 days, biofilm surface area, and applied voltage in BES (-0.8, -1.5 V vs SHE on cathode) was varied. The methane (CH4) production, and microbial biomass growth in TBR and BES was evaluated according to variation of applied conditions. The results demonstrated that relatively faster HRT, resulted in methanation process failure due to biomass washout regarding biofilm detachment in both TBR and BES. The 1:4 CO2/H2 substrate ratio increased (CH4) production in the investigated reactors. In BES, extra CO2 and proton (H+) generation from oxidation reactions can increase CH4 production. However, the lag phase in TBR was shorter than that in BES because of typical greater surface area in TBR. The current density in BES for stoichiometric condition was calculated by using the biomass concentration and biofilm thickness. The current density for stoichiometric condition in BES was -1.3 mA.m-2. In TBR reactor, to consume higher amount of CO2, external H2 should be supplied from another source for converting additional CO2 into CH4 The complexities underline the significant influence of variables on biofilm-based reactors, offering critical insights for experimental process design.

ABSTRACT. Sequencing Batch Reactors (SBR) have become a widely used technology for treating reject water and high strength industrial wastewater treatment. SBR is a fill-and-draw wastewater treatment system where the biological processes are identical to the conventional activated sludge (CAS) process. Knardalstrand wastewater treatment plant in Porsgrunn recently upgraded the plant with two full-scale SBRs. The working volume of the two SBRs are 115 m³ and 100.4 m³, respectively. The main biological process in these two SBR are nitrification and denitrification which takes place simultaneously within one reactor. Modelling and simulation of these two-process using key process control parameters helps to monitor and regulate plant operations efficiently and minimize energy cost. Hence, the objective of this project was to model and simulate the biological process in SBR using GPS-X software. GPS-X is a wastewater modelling and simulation software designed for optimizing upgraded municipal wastewater treatment plants. Two SBR models in GPS-X were developed for ammonia nitrogen (NH₄-N) removal in a simple and advanced model environment. The model simulation result has shown that the advanced SBR model was robust and predicted well the process in the real plant than the simple SBR model. The advanced SBR model’s ammonia removal efficiency through nitrification increased with an increase in dissolved oxygen (DO) concentration. However, the nitrogen mass balance analysis showed that the advanced SBR model had higher nitrogen removal efficiency of 75% at a DO setpoint of 1.5 mg/L through shortcut partial nitrification-denitrification. In all the simulations there was high nitrite (NO₂^-) accumulation in the reactors, which could be due to the partial nitrification coupled with denitrification. The simulation has showed the presence of a simultaneous nitrification-denitrification process in the full-scale SBR plant. We conclude that the accuracy of the advanced SBR model can be improved by quality input data and accurate representation of physical characteristics in the advanced model environment. Moreover, optimization of the simultaneous nitrification and denitrification in the full-scale SBR has a huge potential to reduce the operational energy cost through reduction of DO setpoints.

ABSTRACT. Bioelectrochemical systems (BESs) have garnered extensive research attention for their applications in biosynthesis and environmental remediation. One of the challenges to upscaling BES for carbon dioxide (CO2) methanation is energy-efficient process development. Investigations are ongoing to determine the relationship between the yield of electroactive microorganisms, the key candidate for electrochemical reactions with external electricity input. Consequently, simulating processes, particularly with biocathode for biosynthesis and bioanode for remediation, gives crucial insights for designing efficient BESs. The framework for establishing Nernst-Monod equations for modelling BES, starts from bioanode, where anode respiring bacteria (ARB) oxidate organic carbon compounds to CO2 and generates the proton (H+). In this work, kinetic modelling was applied to calculate the biomass yield of ARBs corresponding to the applied anodic voltage. The generated CO2 and H+ from the anode determined the biomass yield of electroactive methanogens and acetogens on the cathode. Two biofilm models were established for anodic and cathodic biofilm growth in the Aquasim simulation tool. Results showed that the concentration of the organic carbon compound (acetate) available for ARB, had a significant impact on the biofilm thickness and biomass concentration on the biofilm, especially at 0.3 V. The optimum anode voltage which released the highest CO2 and H+, was 0.3 V. The anodic and cathodic biofilm thickness was 3 mm and 55 µm at +0.3V and 10 g.L-1 acetate input to the anode chamber respectively. Moreover, methanogens surpassed acetogens on the biocathode for CO2 reduction to methane rather than acetate. In addition, acetate consumption rate by ARB at anode was remarkably faster than acetate production at cathode.

ABSTRACT. The maritime industry contributes to 80-90% of global trade and is on an increasing trend. However, it is also responsible for substantial amounts of greenhouse gas (GHG) emissions such as carbon dioxide (CO2), nitrogen oxides (NOx), sulfur oxides (SOx), carbon monoxide (CO), and hydrocarbons (HC). Therefore, industries are searching for alternative solutions to reduce GHG emissions by using alternative fuels. This study presents a novel investigation exploring the performance of various alternative marine fuels such as liquefied natural gas (LNG), methanol (MeOH), ammonia (NH3), and hydrogen(H2) in terms of combustion and emissions. Such comprehensive evaluation is limited in literature, making this study uniquely valuable in contributing to the field. The study assesses the impact of different equivalence ratios on emissions for the studied fuel profiles using Cantera and Aspen HYSYS simulations. Results show that CO2 peaks at the stoichiometric ratio, with CO rising from 0.8 to 1.1. Non-carbon fuels like NH3 and H2 emit fewer GHGs than carbonaceous fuels such as LNG and MeOH. H2 has the highest energy release at 87.21 MJ per kg, while NH3 shows lower emission levels, suggesting its potential as a sustainable maritime fuel. This research emphasizes the significance of choosing the right fuel to mitigate maritime emissions, highlighting NH3 and H2 as promising alternatives