The concept of Fuzzy Time Series to predict things that will happen based on the data in the past... more The concept of Fuzzy Time Series to predict things that will happen based on the data in the past, while Markov Chain assist in estimating the changes that may occur in the future. With methods are used to predict the incidence of natural disasters in the future. From the research that has been done, it appears the change, an increase of each disaster, like a tornado reaches 3%, floods reaches 16%, landslides reaches 7%, transport accidents reached 25% and volcanic eruptions as high as 50%.
An amendment to this paper has been published and can be accessed via a link at the top of the pa... more An amendment to this paper has been published and can be accessed via a link at the top of the paper.
The power system planning problem considering system loss function, voltage profile function, the... more The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search fun...
Some companies convection that is often experienced by the owner of XYZ lies in the lack of infor... more Some companies convection that is often experienced by the owner of XYZ lies in the lack of information provided by each - each branch office ( lack of synchronization between the data center and branch office ). This is because the human resources available in each - each branch office is not sufficient to be able to make a more detailed report to the center. Inadequate reports of each - each branch office , also led to the management does not know for sure about their finances and inventory - each branch . System to be built is a container used to store data from various branch offices . Of these containers will be carried out such an analysis process OLAP ( OnLine Analytical Processing ) and can also be used as reporting tools .Operational data of each branch will be entered into a central database . After that , we will perform an Extraction , Transformation , and Loading to filter the data that will be inserted into the data warehouse . Modeling the model used is the Star . Ar...
Digital image processing in general to makes images that appear converted to a function of light ... more Digital image processing in general to makes images that appear converted to a function of light intensity represented in a two-dimensional plane. The function is a value that will be processed for classification so that the computer is able to recognize the image. Besides classification requires training and testing to produce a small error value and optimal algorithm. The problem of optimization is closely related to the principles and findings of science. Getting the smallest error value by calculating using MAPE for that MAPE calculation is done by using the Detection Rate formula to generalize knowledge in order to find the optimal model. Thus, the application of ANN is very suitable for optimizing classification using the Simple Evolving Connectionist System Method and as the result, the classification of images containing protein with test data is that the eggs work with optimal proof of achieving MAPE without modification of 0.1947% and MAPE which has been modified with the ...
International Journal of Supply Chain Management, 2020
Business metrics in Financial Technology 1500 users spread across North Sumatera Province. The im... more Business metrics in Financial Technology 1500 users spread across North Sumatera Province. The impact of commercial digital business (commercial enterpreneurship and social entrepreneurship) is very large on users who are currently increasing in number. To produce Knowledge Acceleration (KAE) Model using Business Metrics on the impact of Commercial Entrepreneurship and Social Entrepreneurship in their utilization.Uncertainty arising from sustainable business operators by considering aspects of Business Metrics related. MARS an linear regression analysis method nonparametrics intended for statistics with the aim of facilitating research and modeling the relationships of each of the multi variables that arise
Data science is the naming of science that can change when dealing with its subject, big data, in... more Data science is the naming of science that can change when dealing with its subject, big data, into big data science. Extraction as the main task of and based on the definition of data science requires an interpretive way of big data. This interpretation follows the characteristics of big data, namely a review of several problems that arise concerning the characters of big data is as an approach. The goal is that data and information, in information extraction or knowledge extraction from the information space, can well organize as is the case in social networks. This paper aims to provide a brief description of it.
2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA), 2020
Extraction is a way to get knowledge from information space, such as social networks. One efficie... more Extraction is a way to get knowledge from information space, such as social networks. One efficient and concise tool for expressing knowledge related to meaning is to involve the concept of similarity. There are several similarity formulations to approach the same thing from the objects but have different tasks according to their functions. However, the measurement results reveal different meanings, although they remain in a mutually supportive position. Therefore, this paper aims to express the different meanings besides new meanings of the similarity function, which are proven based on the difference between the divisors of the similarity formulation. Different measurements of similarities produce different and new meanings with supporting simulation and clustering to manage big data.
The DOTS strategy is the most effective strategy for controlling TB. The DOTS strategy is an impo... more The DOTS strategy is the most effective strategy for controlling TB. The DOTS strategy is an important element in the international policy program recommended for TB control. This strategy ensures TB patients to take anti-TB drugs correctly, at appropriate dosages and intervals. The implementation of DOTS depends on the settings, facilities, resources and environment. There must be flexibility in implementing the DOTS strategy. The results of the research level of knowledge based on the level of education, obtained data where patients with junior and senior high school education have good knowledge of (54.5%). Obtained men with a good level of knowledge 8 people (72.7%). Whereas in women, there were 3 people who were well-informed (27.3%).Based on the work found that those who work have a sufficient level of knowledge there are 30 people (66.7%). While those who did not work had 1 person lacking knowledge and there were 15 people who were knowledgeable (33.3%). Based on the latest l...
Recently, much attention was paid to the application of renewable energy in environmental issues.... more Recently, much attention was paid to the application of renewable energy in environmental issues. Meanwhile, the fuel cell industry, which is considered an environmentally friendly industry, is one of the important components of this project. They are in fact devices for the direct conversion of chemical energy into electrical energy by an electrochemical reaction without the need for any mechanical parts. In this study, it is attempted to model one of their important types, called proton exchange membrane fuel cells, so that it can be used in predicting the behavior of the fuel cell and examining various parameters affecting the performance of the cell. The main idea is to optimal parameters estimation for the proton exchange membrane fuel cells by minimizing the total Squared Error value between the empirical output voltage and the approximated output voltage. For giving better results in terms of accuracy and reliability, a new design of a metaheuristic called the balanced Water Strider Algorithm is utilized. The results of the suggested method are finally validated by comparison with several latest optimizers applied on a practical test case. After running all of the optimizers 30 times independently, the proposed method with minimum absolute error equals 3.4831e−4 shows the best results toward the others.
This paper is focused on the application and performance of artificial intelligence in the numeri... more This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this stu...
Abstract One of the challenging conditions in wellbore management is high-pressure, high-temperat... more Abstract One of the challenging conditions in wellbore management is high-pressure, high-temperature wells that apply large expenditures and maintenance costs to petroleum industries. In this regard, drilling fluid rheology and its crucial factors would help engineers to have a fundamental understanding of the most appropriate wellbore management. Moreover, it can help engineers to control the fluid loss phenomenon. This paper aimed to consider different artificial intelligence and machine learning models to predict the drilling fluid density and select the optimum model, which can be extended to field applications. These models are ANFIS (adaptive neuro-fuzzy inference system), PSO-ANFIS (particle swarm optimization-adaptive neuro-fuzzy inference system), LSSVM-GA (least square support vector machine-genetics algorithm), and RBF (radial basis function) algorithm were modeled in the programming era. In LSSVM-GA model, it is evident that the proper linear equation for the prediction of drilling fluid density is “y = 1.0041x + 0.0019” with the correlation factor of (R 2 = 0 .9966). Due to the advantages of RBF algorithm to other genetics algorithm, this algorithm was used in this part to predict the drilling fluid density. Therefore, it is evident that the proper linear equation for the prediction of drilling fluid density is “y = 1.0009x + 0.0034” with the correlation factor of (R 2 = 0 .9999). Consequently, RBF model was selected as the optimum method in drilling fluid prediction. Furthermore, there is a proper match with training and experimental data and the maximum deviation is about -0.006. In ANFIS algorithm, exponential derivation would be preferred to predict the drilling fluid density instead of linear equation. Consequently, the RBF model provide has a good agreement with the experimental drilling fluid density. It is indicated that the model have appropriate accuracy and validity with experimental data.
Hybrid inorganic perovskites (HIPs) have been developed in recent years as new high-efficiency se... more Hybrid inorganic perovskites (HIPs) have been developed in recent years as new high-efficiency semiconductors with a wide range of uses in various optoelectronic applications such as solar cells and light-emitting diodes (LEDs). In this work, we used a first-principles theoretical study to investigate the effects of phase transition on the electronic and optical properties of CsPbI3 pure inorganic perovskites. The results showed that at temperatures over 300 °C, the structure of CsPbI3 exhibits a cube phase (pm3m) with no tilt of PbI6 octahedra (distortion index = 0 and bond angle variance = 0). As the temperature decreases (approximately to room temperature), the PbI6 octahedra is tilted, and the distortion index and bond angle variance increase. Around room temperature, the CsPbI3 structure enters an orthorhombic phase with two tilts PbI6 octahedra. It was found that changing the halogens in all structures reduces the volume of PbI6 octahedra. The tilted PbI6 octahedra causes the ...
Abstract We developed an artificial intelligence simulation method to predict adsorption process ... more Abstract We developed an artificial intelligence simulation method to predict adsorption process for removal of ions from water via an ordered nanostructure adsorbent. Separation of two ions including Ni and Hg were simulated using the developed artificial intelligence model. The studied adsorbent is a nanocomposite ordered structure made of layered double hydroxide/Metal organic framework structure. Some experimental adsorption data was obtained and employed in the artificial intelligence simulation of the adsorption. The model was built based on artificial neural network including two hidden layers, and different efficient activation functions. The training and validation processes of the neural network was conducted considering two inputs, i.e., type of ion (Hg or Ni) and initial concentration of ion in the solution. Moreover, two outputs were designed in the neural network structure including equilibrium concentration of ion in the solution as well as adsorption capacity of nanocomposite. The data of adsorption were used in the training/validation, and high accuracy with R2 > 0.999 was attained for the simulations of adsorption. It was revealed that the developed machine learning simulation can efficiently predict the adsorption behavior of nanocomposite in removal of ions from the solution, and the accuracy of machine learning model is higher than the well-known adsorption isotherm models.
Abstract Foams would play a substantial role in mobility control and are considered an efficient ... more Abstract Foams would play a substantial role in mobility control and are considered an efficient chemical agent to improve the oil recovery factor. Co-injection of foam and carbon dioxide (CO2) would be one of the optimum injectivity scenarios in fractured tight core samples. This paper aimed to experimentally investigate the surfactant alternating gas injection and CO2-foam injection and how to determine the optimum parameters like foam quality, flow rate, and the number of cycles. It is observed that 0.65 is the optimum foam quality for the fractured tight core samples, and by the increase of foam quality, the pressure drop has been decreased. By increasing foam quality, pressure drop increases up to a specific value ( f g = 0 .65), and after this point, pressure drop has been decreased. Moreover, by the increase of injectivity cycles, pressure drop has increased subsequently. It is observed that five cycles are the optimum number of cycles, and after that, there is no pressure drop decrease in the system. Due to the surfactant property to control the mobility ratio, CO2 breakthrough has occurred at 1.2 PV. Its oil recovery factor at breakthrough is about 48%, and the total oil recovery factor is about 65%. CO2-foam were injected into the system, and due to the presence of a foaming agent in CO2, a CO2 breakthrough occurred at 1.8 PV. The total oil recovery factor is about 83% that indicated the efficiency of this scenario.
This investigation scrutinizes the economic features and potential of propylene and methanol prod... more This investigation scrutinizes the economic features and potential of propylene and methanol production from natural gas in Iran because greenhouse gas emissions released by natural gas-based production processes are lower than coal-based ones. Considering the advantage of Iran’s access to natural gas, this study evaluates and compares the economic value of different plans to complete the value chain of propylene production from natural gas and methanol in the form of four units based on three price scenarios, namely, optimistic, realistic, and pessimistic, using the COMFAR III software. Iran has been ranked as the second most prosperous country globally based on its natural gas reserves. Methanol and propylene production processes via natural gas will lower the release of greenhouse gas. This, increasing the investment and accelerating the development of methanol and propylene production units driven by natural gas will lead the world to a low emission future compared to coal-based...
The concept of Fuzzy Time Series to predict things that will happen based on the data in the past... more The concept of Fuzzy Time Series to predict things that will happen based on the data in the past, while Markov Chain assist in estimating the changes that may occur in the future. With methods are used to predict the incidence of natural disasters in the future. From the research that has been done, it appears the change, an increase of each disaster, like a tornado reaches 3%, floods reaches 16%, landslides reaches 7%, transport accidents reached 25% and volcanic eruptions as high as 50%.
An amendment to this paper has been published and can be accessed via a link at the top of the pa... more An amendment to this paper has been published and can be accessed via a link at the top of the paper.
The power system planning problem considering system loss function, voltage profile function, the... more The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search fun...
Some companies convection that is often experienced by the owner of XYZ lies in the lack of infor... more Some companies convection that is often experienced by the owner of XYZ lies in the lack of information provided by each - each branch office ( lack of synchronization between the data center and branch office ). This is because the human resources available in each - each branch office is not sufficient to be able to make a more detailed report to the center. Inadequate reports of each - each branch office , also led to the management does not know for sure about their finances and inventory - each branch . System to be built is a container used to store data from various branch offices . Of these containers will be carried out such an analysis process OLAP ( OnLine Analytical Processing ) and can also be used as reporting tools .Operational data of each branch will be entered into a central database . After that , we will perform an Extraction , Transformation , and Loading to filter the data that will be inserted into the data warehouse . Modeling the model used is the Star . Ar...
Digital image processing in general to makes images that appear converted to a function of light ... more Digital image processing in general to makes images that appear converted to a function of light intensity represented in a two-dimensional plane. The function is a value that will be processed for classification so that the computer is able to recognize the image. Besides classification requires training and testing to produce a small error value and optimal algorithm. The problem of optimization is closely related to the principles and findings of science. Getting the smallest error value by calculating using MAPE for that MAPE calculation is done by using the Detection Rate formula to generalize knowledge in order to find the optimal model. Thus, the application of ANN is very suitable for optimizing classification using the Simple Evolving Connectionist System Method and as the result, the classification of images containing protein with test data is that the eggs work with optimal proof of achieving MAPE without modification of 0.1947% and MAPE which has been modified with the ...
International Journal of Supply Chain Management, 2020
Business metrics in Financial Technology 1500 users spread across North Sumatera Province. The im... more Business metrics in Financial Technology 1500 users spread across North Sumatera Province. The impact of commercial digital business (commercial enterpreneurship and social entrepreneurship) is very large on users who are currently increasing in number. To produce Knowledge Acceleration (KAE) Model using Business Metrics on the impact of Commercial Entrepreneurship and Social Entrepreneurship in their utilization.Uncertainty arising from sustainable business operators by considering aspects of Business Metrics related. MARS an linear regression analysis method nonparametrics intended for statistics with the aim of facilitating research and modeling the relationships of each of the multi variables that arise
Data science is the naming of science that can change when dealing with its subject, big data, in... more Data science is the naming of science that can change when dealing with its subject, big data, into big data science. Extraction as the main task of and based on the definition of data science requires an interpretive way of big data. This interpretation follows the characteristics of big data, namely a review of several problems that arise concerning the characters of big data is as an approach. The goal is that data and information, in information extraction or knowledge extraction from the information space, can well organize as is the case in social networks. This paper aims to provide a brief description of it.
2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA), 2020
Extraction is a way to get knowledge from information space, such as social networks. One efficie... more Extraction is a way to get knowledge from information space, such as social networks. One efficient and concise tool for expressing knowledge related to meaning is to involve the concept of similarity. There are several similarity formulations to approach the same thing from the objects but have different tasks according to their functions. However, the measurement results reveal different meanings, although they remain in a mutually supportive position. Therefore, this paper aims to express the different meanings besides new meanings of the similarity function, which are proven based on the difference between the divisors of the similarity formulation. Different measurements of similarities produce different and new meanings with supporting simulation and clustering to manage big data.
The DOTS strategy is the most effective strategy for controlling TB. The DOTS strategy is an impo... more The DOTS strategy is the most effective strategy for controlling TB. The DOTS strategy is an important element in the international policy program recommended for TB control. This strategy ensures TB patients to take anti-TB drugs correctly, at appropriate dosages and intervals. The implementation of DOTS depends on the settings, facilities, resources and environment. There must be flexibility in implementing the DOTS strategy. The results of the research level of knowledge based on the level of education, obtained data where patients with junior and senior high school education have good knowledge of (54.5%). Obtained men with a good level of knowledge 8 people (72.7%). Whereas in women, there were 3 people who were well-informed (27.3%).Based on the work found that those who work have a sufficient level of knowledge there are 30 people (66.7%). While those who did not work had 1 person lacking knowledge and there were 15 people who were knowledgeable (33.3%). Based on the latest l...
Recently, much attention was paid to the application of renewable energy in environmental issues.... more Recently, much attention was paid to the application of renewable energy in environmental issues. Meanwhile, the fuel cell industry, which is considered an environmentally friendly industry, is one of the important components of this project. They are in fact devices for the direct conversion of chemical energy into electrical energy by an electrochemical reaction without the need for any mechanical parts. In this study, it is attempted to model one of their important types, called proton exchange membrane fuel cells, so that it can be used in predicting the behavior of the fuel cell and examining various parameters affecting the performance of the cell. The main idea is to optimal parameters estimation for the proton exchange membrane fuel cells by minimizing the total Squared Error value between the empirical output voltage and the approximated output voltage. For giving better results in terms of accuracy and reliability, a new design of a metaheuristic called the balanced Water Strider Algorithm is utilized. The results of the suggested method are finally validated by comparison with several latest optimizers applied on a practical test case. After running all of the optimizers 30 times independently, the proposed method with minimum absolute error equals 3.4831e−4 shows the best results toward the others.
This paper is focused on the application and performance of artificial intelligence in the numeri... more This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this stu...
Abstract One of the challenging conditions in wellbore management is high-pressure, high-temperat... more Abstract One of the challenging conditions in wellbore management is high-pressure, high-temperature wells that apply large expenditures and maintenance costs to petroleum industries. In this regard, drilling fluid rheology and its crucial factors would help engineers to have a fundamental understanding of the most appropriate wellbore management. Moreover, it can help engineers to control the fluid loss phenomenon. This paper aimed to consider different artificial intelligence and machine learning models to predict the drilling fluid density and select the optimum model, which can be extended to field applications. These models are ANFIS (adaptive neuro-fuzzy inference system), PSO-ANFIS (particle swarm optimization-adaptive neuro-fuzzy inference system), LSSVM-GA (least square support vector machine-genetics algorithm), and RBF (radial basis function) algorithm were modeled in the programming era. In LSSVM-GA model, it is evident that the proper linear equation for the prediction of drilling fluid density is “y = 1.0041x + 0.0019” with the correlation factor of (R 2 = 0 .9966). Due to the advantages of RBF algorithm to other genetics algorithm, this algorithm was used in this part to predict the drilling fluid density. Therefore, it is evident that the proper linear equation for the prediction of drilling fluid density is “y = 1.0009x + 0.0034” with the correlation factor of (R 2 = 0 .9999). Consequently, RBF model was selected as the optimum method in drilling fluid prediction. Furthermore, there is a proper match with training and experimental data and the maximum deviation is about -0.006. In ANFIS algorithm, exponential derivation would be preferred to predict the drilling fluid density instead of linear equation. Consequently, the RBF model provide has a good agreement with the experimental drilling fluid density. It is indicated that the model have appropriate accuracy and validity with experimental data.
Hybrid inorganic perovskites (HIPs) have been developed in recent years as new high-efficiency se... more Hybrid inorganic perovskites (HIPs) have been developed in recent years as new high-efficiency semiconductors with a wide range of uses in various optoelectronic applications such as solar cells and light-emitting diodes (LEDs). In this work, we used a first-principles theoretical study to investigate the effects of phase transition on the electronic and optical properties of CsPbI3 pure inorganic perovskites. The results showed that at temperatures over 300 °C, the structure of CsPbI3 exhibits a cube phase (pm3m) with no tilt of PbI6 octahedra (distortion index = 0 and bond angle variance = 0). As the temperature decreases (approximately to room temperature), the PbI6 octahedra is tilted, and the distortion index and bond angle variance increase. Around room temperature, the CsPbI3 structure enters an orthorhombic phase with two tilts PbI6 octahedra. It was found that changing the halogens in all structures reduces the volume of PbI6 octahedra. The tilted PbI6 octahedra causes the ...
Abstract We developed an artificial intelligence simulation method to predict adsorption process ... more Abstract We developed an artificial intelligence simulation method to predict adsorption process for removal of ions from water via an ordered nanostructure adsorbent. Separation of two ions including Ni and Hg were simulated using the developed artificial intelligence model. The studied adsorbent is a nanocomposite ordered structure made of layered double hydroxide/Metal organic framework structure. Some experimental adsorption data was obtained and employed in the artificial intelligence simulation of the adsorption. The model was built based on artificial neural network including two hidden layers, and different efficient activation functions. The training and validation processes of the neural network was conducted considering two inputs, i.e., type of ion (Hg or Ni) and initial concentration of ion in the solution. Moreover, two outputs were designed in the neural network structure including equilibrium concentration of ion in the solution as well as adsorption capacity of nanocomposite. The data of adsorption were used in the training/validation, and high accuracy with R2 > 0.999 was attained for the simulations of adsorption. It was revealed that the developed machine learning simulation can efficiently predict the adsorption behavior of nanocomposite in removal of ions from the solution, and the accuracy of machine learning model is higher than the well-known adsorption isotherm models.
Abstract Foams would play a substantial role in mobility control and are considered an efficient ... more Abstract Foams would play a substantial role in mobility control and are considered an efficient chemical agent to improve the oil recovery factor. Co-injection of foam and carbon dioxide (CO2) would be one of the optimum injectivity scenarios in fractured tight core samples. This paper aimed to experimentally investigate the surfactant alternating gas injection and CO2-foam injection and how to determine the optimum parameters like foam quality, flow rate, and the number of cycles. It is observed that 0.65 is the optimum foam quality for the fractured tight core samples, and by the increase of foam quality, the pressure drop has been decreased. By increasing foam quality, pressure drop increases up to a specific value ( f g = 0 .65), and after this point, pressure drop has been decreased. Moreover, by the increase of injectivity cycles, pressure drop has increased subsequently. It is observed that five cycles are the optimum number of cycles, and after that, there is no pressure drop decrease in the system. Due to the surfactant property to control the mobility ratio, CO2 breakthrough has occurred at 1.2 PV. Its oil recovery factor at breakthrough is about 48%, and the total oil recovery factor is about 65%. CO2-foam were injected into the system, and due to the presence of a foaming agent in CO2, a CO2 breakthrough occurred at 1.8 PV. The total oil recovery factor is about 83% that indicated the efficiency of this scenario.
This investigation scrutinizes the economic features and potential of propylene and methanol prod... more This investigation scrutinizes the economic features and potential of propylene and methanol production from natural gas in Iran because greenhouse gas emissions released by natural gas-based production processes are lower than coal-based ones. Considering the advantage of Iran’s access to natural gas, this study evaluates and compares the economic value of different plans to complete the value chain of propylene production from natural gas and methanol in the form of four units based on three price scenarios, namely, optimistic, realistic, and pessimistic, using the COMFAR III software. Iran has been ranked as the second most prosperous country globally based on its natural gas reserves. Methanol and propylene production processes via natural gas will lower the release of greenhouse gas. This, increasing the investment and accelerating the development of methanol and propylene production units driven by natural gas will lead the world to a low emission future compared to coal-based...
Uploads