scholarly journals Simulation of Methane Gas Production Process from Animal Waste in a Discontinuous Bioreactor

2021 ◽  
Vol 11 (6) ◽  
pp. 13850-13859
Keyword(s):  
Laboratory Data ◽  
Gas Production ◽  
Process Equipment ◽  
Animal Waste ◽  
Monod Kinetics ◽  
Initial Concentration ◽  
Methane Gas ◽  
New Energy ◽  
Time Required ◽  
The Mathematical Model

Due to the importance of environmental protection and the need to use new energy and alternative to conventional fuels, renewable energy has received much attention. Due to this necessity, a discontinuous bioreactor producing methane gas from animal waste has been modeled and simulated in this research. Monod kinetics was used to express the relationship between the growth rate of microorganisms and substrate concentration. The fourth-order Rong Kuta numerical method solved the substrate consumption and production of microorganisms and methane gas. The effect of the initial concentration of microorganisms on methane production has also been investigated. The initial concentrations of substrate and microorganisms are 74.51 g/L and 61.1 g/L, respectively. The results of this study showed that the mathematical model deviates about 53.8% from the laboratory data. According to the presented model, the amount of methane produced after 70 days is equal to 29.10 g/L. The decomposition rate of the substrate and methane gas production depends on the substrate's residence time. Increasing the initial concentration of microorganisms produces methane gas in less time. The amount of methane produced is independent of the initial concentration of microorganisms. The model presented in this study can predict the time required to perform the reaction, optimal bioreactor performance, design of relevant process equipment, and increase the scale of equipment, such as storage tank and proper control to produce high purity methane more volume. Suitable in bioreactors.

scholarly journals Mathematical Model for Reducing the Concentration of a Chemical Substance Applicable in the Procedures of Plasmatic Treatment

2019 ◽  
Vol 70 (4) ◽  
pp. 1298-1301
Author(s):  
Ciprian Mihai Gindac ◽  
Ovidiu Horea Bedreag ◽  
Laura Alexandra Nussbaum ◽  
Iulia Bianca Micu Serbu ◽  
Roxana Folescu ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Laboratory Data ◽  
Chemical Substance ◽  
The Body ◽  
Chemical Model ◽  
Mathematical Form ◽  
Initial Concentration ◽  
Plasma Loss ◽  
The One ◽  
The Mathematical Model

The objective was to study the correlation between the mathematical form of a chemical that we want to lower its initial concentration by the regressive method and the purging of the body�s toxic present chemicals that need to be eliminated. We developed a chemical model, by which, to a given volume, with a certain (X - concentration %) dissolved substance in a container, the initial solvent, without solvit, is added (concentration 0%) with an equal rhythm to the one that is lost from the used container. The solution that will be lost will contain less and less concentrations of solvit, compared to the initial value X%. At the same time, the concentration of our chemical model will decrease. We applied a regressive mathematical formula to this model in order to calculate the concentration in the container in each moment. At the same time, we conducted treatment sessions in patients in which certain substances need to be eliminated, a procedure that complies with the described chemical model. We have demonstrated that at the same volume of 0% solvit wash, the substance purging with X% concentration is more effective, if the procedure starts with an initial loss of concentrated substance, with ulterior volume replacement. Laboratory data confirms the mathematical model in patients who started the procedure with plasma loss. The developed chemical model demonstrates that the initial loss of substance, hastens the decrease of the initial concentration, especially as the loss is higher at the beginning of the procedure if we use the same replacement volume without the substance in the initial solution. This model can be applied in plasma treatment methods in order to study the patient�s safety and the amount of plasma the patient can lose at the beginning.

scholarly journals Methane Gas Production by Animal Waste with Garbage

1986 ◽  
Vol 57 (3) ◽  
pp. 216-222
Author(s):  
Toshinori KAMEOKA ◽  
Michio SAKIMOTO ◽  
Youichi INNO
Keyword(s):  
Gas Production ◽  
Animal Waste ◽  
Methane Gas

scholarly journals Methane Emissions from Process Equipment at Natural Gas Production Sites in the United States: Liquid Unloadings

2014 ◽  
Vol 49 (1) ◽  
pp. 641-648 ◽  
Author(s):  
David T. Allen ◽  
David W. Sullivan ◽  
Daniel Zavala-Araiza ◽  
Adam P. Pacsi ◽  
Matthew Harrison ◽  
...  
Keyword(s):  
United States ◽  
Natural Gas ◽  
The United States ◽  
Methane Emissions ◽  
Gas Production ◽  
Process Equipment ◽  
Natural Gas Production

Geomechanical Responses During Gas Hydrate Production Induced by Depressurization

2016 ◽  
Author(s):  
Ah-Ram Kim ◽  
Gye-Chun Cho ◽  
Joo-Yong Lee ◽  
Se-Joon Kim
Keyword(s):  
Methane Hydrate ◽  
Fossil Fuel ◽  
Gas Production ◽  
Field Scale ◽  
Production Well ◽  
Physical Processes ◽  
Hydrate Dissociation ◽  
Thermal Changes ◽  
New Energy ◽  
Hydrate Bearing Sediments

Methane hydrate has been received large attention as a new energy source instead of oil and fossil fuel. However, there is high potential for geomechanical stability problems such as marine landslides, seafloor subsidence, and large volume contraction in the hydrate-bearing sediment during gas production induced by depressurization. In this study, a thermal-hydraulic-mechanical coupled numerical analysis is conducted to simulate methane gas production from the hydrate deposits in the Ulleung basin, East Sea, Korea. The field-scale axisymmetric model incorporates the physical processes of hydrate dissociation, pore fluid flow, thermal changes (i.e., latent heat, conduction and advection), and geomechanical behaviors of the hydrate-bearing sediment. During depressurization, deformation of sediments around the production well is generated by the effective stress transformed from the pore pressure difference in the depressurized region. This tendency becomes more pronounced due to the stiffness decrease of hydrate-bearing sediments which is caused by hydrate dissociation.

The use of cumulative gas production technique to characterize changes in the fermentation characteristics of rumen contents following variable periods of starvation and grazing in dairy cows

1999 ◽  
Vol 69 (3) ◽  
pp. 647-655 ◽  
Author(s):  
P. Chilibrostet ◽  
B. A. Williams ◽  
S. Tamminga ◽  
S. Calabro
Keyword(s):  
Dairy Cows ◽  
Dry Matter ◽  
Bulk Material ◽  
Gas Production ◽  
Rumen Content ◽  
Starvation Period ◽  
Half Time ◽  
Production Technique ◽  
Grazing Experiment ◽  
Time Required

AbstractThe effect of the duration of grazing (experiment 1) and starvation time and placement in the rumen of inert bulk material before grazing (experiment 2), on the rumen content ferment ability, was investigated by means of measuring cumulative gas production. In experiment 1, a comparison was made of four durations of grazing (1, 1·75, 2·50 and 3·25 h) after overnight starvation. Rumen samples taken from the cows after 1 h of grazing had higher values of total accumulated gas with less (P < 0·05) time required to reach the maximum fermentation rate than cows grazed for 3·25 h. Following grazing, a 7·75·h starvation period was imposed on the four treatments. The extent of fermentation was significantly lower (P < 0·01) after starvation than immediately after grazing (49·7 v. 60·8% of incubated dry matter (DM), respectively). Experiment 2 consisted of a factorial combination of two durations of starvation before grazing (16·5 (LS) and 2·5 (SS) h) with the presence or absence in the rumen of 12·5 kg of a synthetic indigestible material. Before grazing the total accumulated gas production was less (P < 0·05) for the LS than for the SS cows. After the grazing session, the total gas of rumen samples from the LS cows was significantly higher (P < 0·05) than for the SS cows.This was in agreement with the observed higher DM intake during grazing and DM rumen pools after grazing in LS cows. For both starvation periods, the presence of inert rumen bulk led to a higher total gas, a shorter half-time and less DM left unfermented. The measurement of fermentation kinetics by cumulative gas production was suitable to detect changes in rumen content fermentation patterns due to the clearance of material from the rumen (effect of starvation) or DM intake during the grazing sessions.

Analytical Study of Hydro-Pneumatic Processes for Gorlov's Power System

1994 ◽  
Vol 208 (1) ◽  
pp. 67-70
Author(s):  
A I Ryazanov
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Power System ◽  
Analytical Study ◽  
Model Tests ◽  
Air Pressure ◽  
Pneumatic Power ◽  
Time Required ◽  
The Mathematical Model

This paper describes the aerohydrodvnamics of processes in chambers of Gorlov's hydro-pneumatic power system. The mathematical model is developed to determine the main parameters of the processes: water and air velocities, air pressure in the chamber, the periods of time required to fill and empty the chambers and the output of energy during the cycle. The results obtained are in agreement with experimental data and model tests.

scholarly journals Evaluation of nutrient content and in-vitro gas production of complete feed based on corn stover (Zea mays) supplemented by mimosa powder and myristic acid

2020 ◽  
Vol 18 (2) ◽  
pp. 191
Author(s):  
Muchamad Muchlas ◽  
Siti Chuzaemi ◽  
Mashudi Mashudi
Keyword(s):  
Carbon Dioxide ◽  
Zea Mays ◽  
Corn Stover ◽  
Myristic Acid ◽  
Block Design ◽  
Gas Production ◽  
Total Carbon ◽  
Methane Gas ◽  
In Vitro Gas Production

<p class="MDPI17abstract"><strong>Objective: </strong>The purpose of this research was to evaluate the effect supplementation of mimosa powder as a source of condensed tannins and a single fatty acid, myristic acid, in a complete feed based on corn stover (<em>Zea mays</em>) using the in-vitro gas production method. This research has been carried out at the Animal Nutrition and Food Laboratory, Faculty of Animal Husbandry, Brawijaya University. The time of the research was conducted in August until December 2019.</p><p class="MDPI17abstract"><strong>Methods: </strong>The experimental design used randomized complete block design by ANOVA consisting four treatments and three replications which were P1= a complete feed based on corn stover (<em>Zea mays</em>) as control Diet (CD) (40% corn stover + 60 % concentrate), P2= (CD) + Mimosa Powder(MP) 1.5 %/kg DM + myristic acid (MA)2% /kg DM, P3= CD + MP 1.5 % /kg DM + MA 3% /kg DM, and P4= CD + MP 1.5 %/kg DM + MA 4 %/kg DM.</p><p class="MDPI17abstract"><strong>Results: </strong>The results showed that the treatments affected total gas production (p&lt;0.01). The highest value for total gas production was found in P1 (86.67 ml/500 mg DM) and the lowest was found in P3 (73.30 ml/500 mg DM). The results showed that gas production decreased concurrently with the increase of MA level. In vitro methane gas and carbon dioxide production was showed different (p&lt;0.05) from the control treatment. The lowest concentration of methane production was in P4 (82863.07 ppm) and the highest concentration was in treatment P1 86530.89 ppm. The highest total carbon dioxide content was P1 (436711.57 ppm) and the lowest concentration was P3 (350287.72 ppm).</p><p class="MDPI17abstract"><strong>Conclusions: </strong>The results of the research concluded that the addition of mimosa powder and 3 different levels of myristic acid in a complete feed based on corn stover can increase the nutritional value of a complete feed and reduce the production of methane gas.</p>

scholarly journals Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation (Preprint)

2019 ◽  
Author(s):  
Helmut Spengler ◽  
Claudia Lang ◽  
Tanmaya Mahapatra ◽  
Ingrid Gatz ◽  
Klaus A Kuhn ◽  
...  
Keyword(s):  
Laboratory Data ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Use Cases ◽  
Iterative Refinement ◽  
Cloud Infrastructure ◽  
Specific Domain ◽  
Wide Range ◽  
Data Loading ◽  
Time Required

BACKGROUND Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. OBJECTIVE Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. METHODS We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. RESULTS The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. CONCLUSIONS Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation. CLINICALTRIAL

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