scholarly journals Biomass to Syngas: Modified Stoichiometric Thermodynamic Models for Downdraft Biomass Gasification

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5383
Author(s):  
Hafiz Muhammad Uzair Ayub ◽  
Sang Jin Park ◽  
Michael Binns
Keyword(s):  
Fossil Fuels ◽  
Equilibrium Constants ◽  
Operating Conditions ◽  
Model Parameters ◽  
Correction Factors ◽  
Thermodynamic Models ◽  
Renewable Biomass ◽  
Systems Model ◽  
Gasification Process ◽  
Different Types

To help meet the global demand for energy and reduce the use of fossil fuels, alternatives such as the production of syngas from renewable biomass can be considered. This conversion of biomass to syngas is possible through a thermochemical gasification process. To design such gasification systems, model equations can be formulated and solved to predict the quantity and quality of the syngas produced with different operating conditions (temperature, the flow rate of an oxidizing agent, etc.) and with different types of biomass (wood, grass, seeds, food waste, etc.). For the comparison of multiple different types of biomass and optimization to find optimal conditions, simpler models are preferred which can be solved very quickly using modern desktop computers. In this study, a number of different stoichiometric thermodynamic models are compared to determine which are the most appropriate. To correct some of the errors associated with thermodynamic models, correction factors are utilized to modify the equilibrium constants of the methanation and water gas shift reactions, which allows them to better predict the real output composition of the gasification reactors. A number of different models can be obtained using different correction factors, model parameters, and assumptions, and these models are compared and validated against experimental data and modelling studies from the literature.

scholarly journals Quality assessment of gas produced from different types of biomass pellets in gasification process

2019 ◽  
Vol 38 (2) ◽  
pp. 406-416 ◽  
Author(s):  
Marcel Mikeska ◽  
Jan Najser ◽  
Václav Peer ◽  
Jaroslav Frantík ◽  
Jan Kielar
Keyword(s):  
Gas Turbines ◽  
Combustion Engine ◽  
Calorific Value ◽  
Internal Combustion ◽  
Combustion Gas ◽  
Gas Cleaning ◽  
Gasification Process ◽  
Different Types ◽  
Before And After ◽  
Cleaning Technology

Gas from the gasification of pellets made from renewable sources of energy or from lower-quality fuels often contains a number of pollutants. This may cause technical difficulties during the gas use in internal combustion gas engines used for energy and heat cogeneration. Therefore, an adequate system of gas cleaning must be selected. In line with such requirements, this paper focuses on the characterization and comparison of gases produced from different types of biomass during gasification. The biomass tested was wood, straw, and hay pellets. The paper gives a detailed description and evaluation of the measurements from a fix-bed gasifier for the properties of the produced gases, raw fuels, tar composition, and its particle content before and after the cleaning process. The results of elemental composition, net calorific value, moisture, and ash content show that the cleaned gases are suitable for internal combustion engine-based cogeneration systems, but unsuitable for gas turbines, where a different cleaning technology would be needed.

scholarly journals Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 463
Author(s):  
Gopinathan R. Abhijith ◽  
Leonid Kadinski ◽  
Avi Ostfeld
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Mechanistic Model ◽  
Water Distribution Systems ◽  
Sensitivity Study ◽  
Operating Conditions ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Bacterial Regrowth ◽  
The Impact

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

scholarly journals Renewable Biomass Utilization: A Way Forward to Establish Sustainable Chemical and Processing Industries

2021 ◽  
Vol 3 (1) ◽  
pp. 243-259
Author(s):  
Yadhu N. Guragain ◽  
Praveen V. Vadlani
Keyword(s):  
Fossil Fuels ◽  
Process Intensification ◽  
Raw Material ◽  
Biomass Composition ◽  
Biomass Feedstocks ◽  
Renewable Biomass ◽  
Sustainability Criteria ◽  
Biomass Component ◽  
Biomass Resources ◽  
Multiple Challenges

Lignocellulosic biomass feedstocks are promising alternatives to fossil fuels for meeting raw material needs of processing industries and helping transit from a linear to a circular economy and thereby meet the global sustainability criteria. The sugar platform route in the biochemical conversion process is one of the promising and extensively studied methods, which consists of four major conversion steps: pretreatment, hydrolysis, fermentation, and product purification. Each of these conversion steps has multiple challenges. Among them, the challenges associated with the pretreatment are the most significant for the overall process because this is the most expensive step in the sugar platform route and it significantly affects the efficiency of all subsequent steps on the sustainable valorization of each biomass component. However, the development of a universal pretreatment method to cater to all types of feedstock is nearly impossible due to the substantial variations in compositions and structures of biopolymers among these feedstocks. In this review, we have discussed some promising pretreatment methods, their processing and chemicals requirements, and the effect of biomass composition on deconstruction efficiencies. In addition, the global biomass resources availability and process intensification ideas for the lignocellulosic-based chemical industry have been discussed from a circularity and sustainability standpoint.

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

Robustness enhancement of biomass steam gasification thermodynamic models for biohydrogen production: Introducing new correction factors

2021 ◽  
pp. 128954
Author(s):  
Hafiz Muhammad Uzair Ayub ◽  
Muhammad Abdul Qyyum ◽  
Kinza Qadeer ◽  
Michael Binns ◽  
Ahmed Tawfik ◽  
...  
Keyword(s):  
Steam Gasification ◽  
Biohydrogen Production ◽  
Correction Factors ◽  
Thermodynamic Models

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

2021 ◽  
pp. 1-18
Author(s):  
Gisela Vanegas ◽  
John Nejedlik ◽  
Pascale Neff ◽  
Torsten Clemens
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Numerical Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Large Set ◽  
Model Parameter ◽  
Production History ◽  
Hydrocarbon Fields ◽  
Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

Study On Automatic Adaptation for Control-Oriented Model of Advanced Diesel Engine

2021 ◽  
Author(s):  
Shunki Nishii ◽  
Yudai Yamasaki
Keyword(s):  
Operating Conditions ◽  
Physical Models ◽  
Model Parameters ◽  
Training Methods ◽  
Model Based Control ◽  
Practical Applications ◽  
Model Based ◽  
Automatic Adaptation ◽  
Automobile Engines ◽  
Adaptation Method

Abstract To achieve high thermal efficiency and low emission in automobile engines, advanced combustion technologies using compression autoignition of premixtures have been studied, and model-based control has attracted attention for their practical applications. Although simplified physical models have been developed for model-based control, appropriate values for their model parameters vary depending on the operating conditions, the engine driving environment, and the engine aging. Herein, we studied an onboard adaptation method of model parameters in a heat release rate (HRR) model. This method adapts the model parameters using neural networks (NNs) considering the operating conditions and can respond to the driving environment and the engine aging by training the NNs onboard. Detailed studies were conducted regarding the training methods. Furthermore, the effectiveness of this adaptation method was confirmed by evaluating the prediction accuracy of the HRR model and model-based control experiments.

scholarly journals The Optimization of Hybrid Power Systems with Renewable Energy and Hydrogen Generation

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1948 ◽  
Author(s):  
Fu-Cheng Wang ◽  
Yi-Shao Hsiao ◽  
Yi-Zhe Yang
Keyword(s):  
Power Systems ◽  
System Performance ◽  
Hydrogen Generation ◽  
System Optimization ◽  
Energy Sources ◽  
Model Parameters ◽  
Power Model ◽  
Hybrid Power Systems ◽  
Hybrid Power ◽  
Different Types

This paper discusses the optimization of hybrid power systems, which consist of solar cells, wind turbines, fuel cells, hydrogen electrolysis, chemical hydrogen generation, and batteries. Because hybrid power systems have multiple energy sources and utilize different types of storage, we first developed a general hybrid power model using the Matlab/SimPowerSystemTM, and then tuned model parameters based on the experimental results. This model was subsequently applied to predict the responses of four different hybrid power systems for three typical loads, without conducting individual experiments. Furthermore, cost and reliability indexes were defined to evaluate system performance and to derive optimal system layouts. Finally, the impacts of hydrogen costs on system optimization was discussed. In the future, the developed method could be applied to design customized hybrid power systems.

Optimization of a Biomass Micro-Gasification Process for the Production of Synthesis Gas From Palm Shell

2015 ◽  
Author(s):  
Luz M. Ahumada ◽  
Arnaldo Verdeza ◽  
Antonio J. Bula
Keyword(s):  
Particle Size ◽  
Fixed Bed ◽  
High Energy ◽  
Operating Conditions ◽  
Air Velocity ◽  
Output Variable ◽  
Heating Value ◽  
Palm Shell ◽  
Gasification Process ◽  
Air Speed

This paper studied, through an experiment design, the significance of particle size, air speed and reactor arrangement for palm shell micro-gasification process in order to optimize the heating value of the syngas obtained. The range of variables was 8 to 13 mm for particle size, 0.8–1.4m/s for air velocity, and updraft or downdraft for the reactor type. It was found that the particle size and air velocity factors were the most significant in the optimization of the output variable, syngas heating value. A heating value of 2.69MJ / Nm3 was obtained using a fixed bed downdraft reactor, with a particle size of 13 mm and 1.4 m/s for air speed; verification of the optimum point of operation under these conditions verified that these operating conditions favor the production of a gas with a high energy value.

Using Sliding Mode Control to Adjust Drum Level of a Boiler Unit With Time Varying Parameters

2010 ◽  
Author(s):  
Hamed Moradi ◽  
Firooz Bakhtiari-Nejad ◽  
Majid Saffar-Avval ◽  
Aria Alasty
Keyword(s):  
Water Level ◽  
Transfer Functions ◽  
Sliding Mode ◽  
Operating Conditions ◽  
Model Parameters ◽  
Feed Water ◽  
Time Varying ◽  
Boiler Unit ◽  
Varying Parameters ◽  
Time Varying Parameters

Stable control of water level of drum is of great importance for economic operation of power plant steam generator systems. In this paper, a linear model of the boiler unit with time varying parameters is used for simulation. Two transfer functions between drum water level (output variable) and feed-water and steam mass rates (input variables) are considered. Variation of model parameters may be arisen from disturbances affecting water level of drum, model uncertainties and parameter mismatch due to the variant operating conditions. To achieve a perfect tracking of the desired drum water level, two sliding mode controllers are designed separately. Results show that the designed controllers result in bounded values of control signals, satisfying the actuators constraints.

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