Aayush Panthi

Aayush Panthi

Chemical Engineer | Process Engineer | Data-Driven Industrial Solutions
📍 Nepal

Profile

Process Engineer with hands-on experience in cement manufacturing and industrial process optimization. Skilled in process control, plant operations, and quality management, with additional expertise in applying data-driven and machine learning techniques to environmental and process systems. Demonstrated leadership through national-level technical initiatives and delivery of practical engineering solutions.

Core Expertise

• Process Optimization & Plant Operations (VRM, Kiln, Mills)
• Process Control & Quality Assurance
• Industrial Safety & Risk Management
• Water & Wastewater Treatment Systems
• Machine Learning for Process & Environmental Data
• Technical Analysis & Cross-functional Collaboration

Professional Experience

Process Engineer – Shivam Cement Limited | Dec-2025 - Present

• Optimize and monitor a 1,900 TPD clinker production plant to ensure stable and efficient performance.
• Analyze and control key process parameters across vertical roller mill and ball mill systems, including temperature profiles, draft measurements, and airflow behavior.
• Drive coal consumption reduction and specific heat optimization as part of the plant energy-saving team.
• Conduct false-air testing across the pyro-process and grinding circuits to identify leakage points and support heat consumption optimization.
• Maintain product quality by managing residue, fineness, and clinker quality targets.
• Contribute process data and analysis to daily, weekly, and monthly production, stoppage, and RCA reports.
• Collaborate with maintenance and production teams to reduce downtime and improve process reliability.y

Intern – Nepal Oil Corporation (NOC) | 2024

• Worked on petroleum storage, fuel quality testing, and distribution operations
• Gained exposure to HSE standards, inspection procedures, and regulatory compliance

Machine Learning Research (Academic Project) | 2024

• Developed predictive models for wastewater treatment parameters using RNN
• Applied data-driven techniques for process monitoring and environmental analysis