🔐 Tutorial of setting up Security for your API with one way authentication with TLS/SSL and mutual authentication for a java based web server and a client with both Spring Boot. Different clients are provided such as Apache HttpClient, OkHttp, Spring RestTemplate, Spring WebFlux WebClient Jetty and Netty, the old and the new JDK HttpClient, the old and the new Jersey Client, Google HttpClient, Unirest, Retrofit, Feign, Methanol, vertx, Scala client Finagle, Featherbed, Dispatch Reboot, AsyncHttpClient, Sttp, Akka, Requests Scala, Http4s Blaze, Kotlin client Fuel, http4k, Kohttp and ktor. Also other server examples are available such as jersey with grizzly. Also gRPC, WebSocket and ElasticSearch examples are included

⚗️ Lightweight HTTP extensions for Java & Kotlin

Tutorial to run AMBER umbrella COM restraint code and derive free energy of transfer profile for methanol through DMPC membrane

Progressive methanol injection controller for BMW E92 and similar cars. The methanol delivery rate is determined by querying the current fuel load from the ECU using the UDS protocol over CANbus (via the OBD port).

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Dimethyl ether/Methanol to Olefins (DMTO) is one of the important unit in coal chemical industry, and the distribution of its reaction products can be regulated and optimized by catalyst carbon deposition. Aiming at the disadvantages of time-consuming and high-cost analysis of traditional catalyst carbon deposition measurement methods, a Just-in-Time Learning (JITL) soft sensor model based on Euclidean Distance similarity and Bayesian Regression local model (EDBR-JITL) is proposed for DMTO catalyst carbon deposition in this paper. In the proposed model, the mapping relationship between process state, operating parameters, and catalyst carbon deposition is established, and catalyst carbon deposition is predicted at sampling time by a linear local model (BR) which is obtained by the ED similarity. The results of an industrial DMTO example show that the mean absolute error (MAE) between the predicted value of EDBR-JITL online and the real value of measurement offline for catalyst carbon deposition is 0.10, the mean absolute percentage error (MAPE) is 1.46%, the coefficient of determination (R 2 ) is 0.92, and the prediction time is only in milliseconds. In summary, catalyst carbon deposition in DMTO process is not only predicted and monitored online accurately and conveniently by the proposed EDBR-JITL method, but also is regulated and optimized as the linear BR local model can return the weight of each input parameter at each sampling time.

Molecular Dynamics Simulation of Water-Methanol Mixture to determine physical properties like self-diffusion coefficient, density and shear viscosity for a system consisting of 216 molecules of water and 216 molecules of methanol

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Deep-DMS-Reactor is a deep learning model for optimizing a tubular plug flow reactor in the methanol-hydrogen sulfide to DMS process.

Mathematical optimization model for Cost-Competitive Offshore Wind-Powered Green Methanol Production for Maritime Transport Decarbonization

This is a collection of Matlab codes to calculate various parameters for a distillation column design for an acetone-methanol feed solution

This repository contains the complete code of the forecast-sites modelling approach for technology diffusion scenarios of energy-intensive industries. Within this repository also a first part of the Fraunhofer ISI IndustrialSiteDatabase for European primary steel and basic chemical (high value chemicals (HVC, ammonia, and methanol)) is published.

The Methanol is a page load benchmarking engine.

methanol+1-octanol VLE

GLS greenbubble is a open model of the PtX section of the GreenLab Skive industrial hub, developed in the PyPSA framework. This model allows for capacity and dispatch optimization of the GreenLab Skive Power-to-X site for driven by demands for H2 and Methanol

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No-fuss document site builder with rEFui and MDX

Aspen Plus Simulation of Methanol Production Plant

Methanol with carbon cycling for ultra-long-duration energy storage

Analysis of a Methanol Synthesis Process with Python, Cantera.

DIST Dataset: A detailed month-long data collection from a methanol distillation process

Version of Methanol web crawler that exposes file-writing capabilities for the JavaScript workers

CO2 Hydrogenation to Methanol Tubular Packed Bed Reactor

This Python script simulates a hybrid solar and wind farm using pvlib and windpowerlib. It fetches NASA POWER API data to model combined energy output. The code then analyzes the energy balance against an electrolyzer and methanol plant's consumption, calculating the net grid surplus or deficit and visualizing the results.

Jupyter Hub Code for the multi strategy optimization of Reaction Kinetics for Methanol Synthesis

An enzyme mining process based on python and multiple bioinformatics tools and exemplifies its application to methanol oxidase (AOX).

Research project on methanol steam reforming using Zr-HMS catalyst to enhance hydrogen yield, increase diesel cetane number, and reduce emissions.

This Python program implements the method described in the paper 'Machine Learning for Time-Resolved Selectivity Analysis in Methanol-To-Olefins Reaction'.

code for paper "A Method for Obtaining Liquid−Solid Adsorption Rates from Molecular Dynamics Simulations: Applied to Methanol on Pt(111) in H2O", https://dx.doi.org/10.1021/acs.jctc.9b01249

MethanolFuel's Street Legal build is finally on GitHub! And of course, he's ready to serve. The REAL modding SLRR is here.