RABBIT MINER
Rabbit Miner Ltd. offers comprehensive data mining and process systems engineering services. We are specialized in process optimization, data science applications, supply chain management, and Industry 4.0 solutions. Our potential partners and customers are the industrial companies open to new data-driven optimization techniques. Our experience in data science as well as in process systems engineering makes us the ideal innovation and R&D service partner to solve your process optimization problems.
Solutions
AI-BASED ALARM MANAGEMENT
Risk mitigation by Artificial Intelligence
DATA-DRIVEN PROCESS DEVELOPMENT
Identification of process-models
SMART MONITORING - INTELIGENT SPACE
Analysing real-time position and machine data
ROOT CAUSE ANALYZES BASED ON MACHINE LOGS
Critical event analayses and predictive maintenance
DECISION SUPPORT - REAL-TIME DIGITAL TWIN
Applied Industry 4.0 solutions in production systems
RABBIT MINER IN TOP 50 IN BIG BANG STARTUP COMPETITION
The process systems engineers
According to a recent study, companies who can effectively quantify gains from analyzing data, report an average of 10 % reduction in costs each year, moreover, data analysis helps companies investing capitals more efficiently.
Therefore, industrial plants and production sites record more and more data of the production due to the increasing sensor technology.
However, only a minority of this data is used to increase production efficiency and excellence.
years experience in data-based solutions
Latest News
SEQUENCE MINING BASED ALARM SUPPRESSION
Despite the high-pace improvement of industrial process automation, the management of abnormal events still requires human actions.
HIERARCHICAL FREQUENT SEQUENCE MINING ALGORITHM FOR THE ANALYSIS OF ALARM CASCADES IN CHEMICAL PROCESSES
Faults and malfunctions on complex chemical production systems generate alarm cascades that hinder the work of the operators and make fault diagnosis a complex and challenging task.
UNDERSTANDING THE IMPORTANCE OF PROCESS ALARMS BASED ON THE ANALYSIS OF DEEP RECURRENT NEURAL NETWORKS TRAINED FOR FAULT ISOLATION
The identification of process faults is a complex and challenging task due to the high amount of alarms and warnings of control systems.
About Us
GYULA DÖRGŐ
Gyula graduated with bachelor’s (2016) and master’s (2017) degree in chemical engineering with a specialization in process engineering from the University of Pannonia. His work interest covers the areas of goal-oriented optimization, event analysis and data science applications in process mining. His current work focuses on the applications of process mining tools in alarm management for the improvement of process safety.
TAMÁS RUPPERT
Tamás graduated with a bachelor’s degree in Mechanical Engineering (2015) and Engineering Information Technology (2015) and master’s degree in Mechatronical Engineering (2016). His work interests cover the areas of process mining algorithms, Discrete-event simulators and supply chain management. His current work focuses on Industry 4.0 (Discrete Event simulators, Connected Factory, Supply Chain management) and Big Data.
