c common functions library extracted from my open source project FastDFS. this library is very simple and stable. functions including: string, logger, chain, hash, socket, ini file reader, base64 encode / decode, url encode / decode, fast timer, skiplist, object pool etc. detail info please see the c header files.

A C# DLL injection library

A simple mock-application for programs that work with child processes

Collections of Studio 5000 AOIs, UDTs, general design patterns, and sample programs.

CHBE 356 - Process Dynamics and Control

Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach

Windows 10 Stand-Alone Application: Create SVG SCADA Drawings

A sophisticated brewery control dashboard using node-red

Process Dynamics Engine (PDE) is an online, real-time simulator for process control models described by transfer functions or state space representations.

Classic problems in chemical engineering solved with matlab

A C# process filter driver example which was implemented with the Process Filter Driver SDK. The Process Filter Driver SDK is a kernel-mode driver that filters process/thread creation and termination, it provides you an easy way to develop Windows application for the Windows process monitoring and protection.

PyChemEngg is a python-based framework to promote problem solving and critical thinking in chemical engineering.

A process manager with an HTTP API for console and file access.

Pectus is a process control system for Unit Operations such as filtration, chromatography, precipitation, solubilization and refolding. Pectus implements a language called P-code which is used to write methods and control components on the Unit Operation.

Para Injeção de Codigos e DLL's em Processos

Hybrid machine learning framework for modelling and control of particle processes

Reconstruction of several papers on the topic of steady-state detection/identification.

This project is source code of paper Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning by X. Zhang, K. Zhang, Z. Li, and X. Yin. The objective of this work is to learn the DeePC operator using a neural network and bypass online optimization of conventional DeePC for efficient online implementation.

pipex a simple rewrite of the standard |