Gas-main vs Cluster2A Usage & Stats
The application allows the user to solve most of gas transportation problems, calculate the process of transporting natural gas (steam phase of LNG, LPG, associated petroleum gas, biogas) through a single-line pipeline.
The graphical representation allows to instantly find the optimal solution and determine the influence of various factors on the gas transportation process. The application can help the user to get a calculation result that is sufficient for making an estimated engineering decision.
Scope of application
• Distribution and internal gas supply systems
• Main-gas transport
• Gas distribution and gas collection networks
Key Features
Using the application can be calculated:
• gas flow rate in the pipeline for a given pressure drop and geometric characteristics of the pipeline
• pressure drop in the gas pipeline (final or initial pressure) for a given flow rate and pressure drop
• the length of the gas pipeline that provides a specified gas flow rate for a given pressure drop and diameter of the gas pipeline
• the required diameter of the gas pipeline, which provides a specified gas flow rate for a given pressure drop and length of the gas pipeline
• the blowdown time for stopped section of gas pipeline
• the required power of the gas compressor, which provides the necessary degree of pressure increase for a specified natural gas flow rate. The compression process is adiabatic. The calculation can be performed for a single-or multi-stage compressor.
The application uses: General Fundamental Isothermal Flow Equation (the basic equation of gas flow), American Gas Association Equation (AGA NB-13 method), Weymouth Isothermal Flow Equation, Panhandle A Isothermal Flow Equation. Panhandle B Isothermal Flow Equation, Institute of Gas Technology Isothermal Flow Equation. The user can choose one of them.
The properties of the transported gas are determined on base of its composition. The user can select the gas composition from the previously entered ones, edit, add a new one, or delete the previously entered gas composition.
The user can also select the pipe wall material and, accordingly, the absolute roughness value from the previously entered ones, edit, add a new value, or delete the previously entered material name and roughness value.
For the convenience of calculations, the application provides a wide range of applicable units of measurement, as well as standard conditions for measuring gas flow.
The accuracy of the calculation methods used in the application corresponds to or exceeds the accuracy of the methods for obtaining initial data available in engineering practice.
The data entered by the user is saved and transferred between the calculation forms, which allows the user to quickly consider the problem of calculating the parameters of the gas pipeline from all sides.
The results can be saved as a pdf file, as well as printed out.
Terms of use: https://www.gas-main.info/#terms-of-use
Privacy Policy: https://www.gas-main.info/#privacy-policy
- Apple App Store
- Free
- Productivity
Store Rank
- -
For most people, it is difficult to obtain the information they need directly from raw data. Machine learning can transform disordered data into useful information. Clustering is an unsupervised machine learning technique that groups similar objects into the same cluster. Cluster2A combines the two most popular clustering algorithms, K-means and DBSCAN, to help you discover interesting patterns in the data.
For example, Cluster2A can perform cluster analysis based on customer consumption behavior and provide results for customer segmentation. Customer segmentation is the use of specific characteristics to identify and organize customers. These characteristics can be demographic, behavioral/psychological characteristics and geographic location. Customer segmentation can identify customers and provide products and services tailored to their needs. This personalization will provide you with a competitive advantage, increase customer conversion rates and brand loyalty.
K-means Model:
The K-means algorithm requires the number of clusters to be specified. The main goal is to find a representative data point (called centroid) in a large amount of high-dimensional data, and then assign each data point to the nearest centroid.
DBSCAN Model:
Unlike K-means, DBSCAN does not need to specify the number of clusters to be generated. The DBSCAN algorithm processes data points based on density, mainly dividing sufficiently dense points in the feature space into the same cluster, and can identify outliers that do not belong to any cluster, which is very suitable for detecting outliers.
Growth data type:
You can select time series data with 12 periods, 24 periods, and 36 periods for analysis.
The most commonly used data are monthly material purchase prices, monthly product sales, monthly customer purchases, and the company's annual operating income.
For example, you can perform cluster analysis based on the monthly purchase data of VIP customers. Cluster2A will automatically calculate each customer's purchase growth rate, purchase volatility and the growth rate per unit of volatility, and make clustering recommendations.
Feature data type:
You can select 2 to 10 characteristics for analysis.
The most commonly used characteristics are as follows: Demographics: For example, age, gender, income, education, nationality and family size. Behavior/Psychology: For example, consumption style (RFM model) and personality type (DISC model). Geography: For example, country, region and city. Statistics/Finance: For example, mean, standard deviation, Sharpe ratio, β, α and R-squared.
For example, you can perform cluster analysis based on the three buying characteristics of customers, RFM (Recency, Frequency, Monetary).
- Apple App Store
- Free
- Productivity
Store Rank
- -
Gas-main VS. 
Cluster2ADecember 31, 2024