Advanced Project Engr

The key expectation is to combine traditional process engineering expertise with advanced digital and analytical skills to drive efficiency, innovation, and safety. Project domains - Chemical / Refinery / Petrochemical / Oil & Gas customers of Honeywell within & outside India. Active contribution in Project estimation & costing , developing compliance to customer RFQ & Standards, managing Project execution teams of Honeywell & vendor, interface with Honeywell product development as needed during product feature enhancement/ validation, perform customer facing activities such as Progress reviews & reporting .

Expectations in 3D plant modeling Projects:

In 3D modeling projects, which create a digital "twin" of a plant, process engineers are expected to provide the foundational process data and ensure the digital model accurately reflects real-world operational needs. Must be aware of process plant interface with data from control systems like DCS and PLCs.

Data and design authority

• Generate key documents: Provide essential process design documents such as Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) to guide the 3D layout.
• Validate the model: Review and approve the 3D model to ensure the placement of equipment and piping is functionally correct, safe, and complies with engineering standards.
• Input functional requirements: Specify process-critical details like pipe thickness, line routing, material specifications, and instrument placement. The 3D model must accommodate factors like flow characteristics and maintenance access.
• Perform simulations: Use the 3D model with simulation software to run virtual tests.

Quality and safety integration

• Perform clash detection: Collaborate with other disciplines (e.g., mechanical, civil, electrical) to automatically or manually identify and resolve clashes in the model. The process engineer focuses on issues that could impact process flow or safety.
• Conduct safety studies: Utilize the 3D environment to perform studies such as Hazard and Operability (HAZOP) analysis, assessing potential risks in a detailed, visual context.
• Drive continuous improvement: Use the 3D model to test upgrades and changes to existing plant layouts, ensuring they lead to a more efficient and higher-quality process.

Expectations in AI modeling projects

AI projects focus on using plant data to build predictive models that optimize operations. The process engineer acts as a subject matter expert who translates operational needs into AI-ready problems and validates the solutions.

Data management and preparation

• Ensure data quality: Validate the accuracy and completeness of historical and real-time plant data, which is critical for training reliable AI models. This requires a deep understanding of the plant's sensors and operational history.
• Identify relevant data: Determine which process variables and operating conditions are most important for the AI model to track and analyze to achieve a specific goal, like predicting equipment failure or optimizing yield.
• Provide process knowledge: Use fundamental engineering knowledge to clean and structure data, providing context that helps the AI model learn complex process behaviors.

Model development and application

• Lead process optimization: Collaborate with AI specialists to develop models that recommend optimal operating parameters (e.g., temperature, flow rate, pressure) to improve production efficiency and reduce energy consumption.
• Implement predictive maintenance: Work with predictive AI models that analyze sensor data to detect early signs of equipment wear. The engineer's role is to act on these predictions by scheduling maintenance to prevent costly downtime.
• Enhance safety and risk management: Contribute to AI-powered systems that predict potential process upsets or safety hazards. The engineer provides the critical process knowledge necessary to build accurate and reliable safety-related models.
• Validate model results: Critically evaluate the output of AI models. The engineer must ensure that AI-generated recommendations are practical, safe, and aligned with core engineering principles.

• Qualification: BE/B.Tech Chemical or Petrochemical or Instrumentation
• Experience : 8 to 10 yrs of experience in process plant (Chemical / Oil & Gas / refiner/ Petrochemical)

Technical Skills :

• Knowledge of Process simulation/OTS will be added advantage
• Knowledge of AR/VR training Simulators will be an added advantage
• Exposure to distributed control systems (DCS) and safety instrumented systems (SIS) as a user/ configuration/ integration with simulation
• Must have experience with Process plant equipment operation (Start/ stop/trip/ shutdown/ sequence/ interlocks/ alarms/ safety)
• Must have experience in Process Abnormalities management (detection / prediction /recovery procedure guidance & implementation)
• Soft Skills :
• Strong interpersonal and communication skills to effectively translate complex technical concepts between process operators, engineers from other disciplines, and data scientists.
• Data Analytical mindset
• The willingness to adopt iterative, flexible development processes to keep pace with rapid technological advancements