Renewable steam cycles. InCycle: the methodology for a global optimization

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Renewable steam cycles. InCycle: the methodology for a global optimization

Current problems for owners and operators of renewable energy plants based on a steam cycle are usually related to the use of equipment that has been in operation for a long time. Most of the time, such equipment is conditioned by previous repairs, loss of efficiency, and deviations between the initial design and real operating conditions.

Besides these common issues, specifically, CSP plants (Concentrated Solar Power plants) are mechanically conditioned by the actual operation of its power cycles, which involves daily startup/shutdown cycling operations and fosters main equipment fouling and corrosion. For these reasons, a new approach is necessary to attack the operation and maintenance activities of concentrated solar plants.

Renewable steam cycles. InCycle: the methodology for a global optimization

‘InCycle’ analysis methodology allows to systematically and specifically address the condition of steam cycle plants and its key factors, to improve their efficiency, increase their service life, and reduce regular failures and shutdowns for maintenance.


  • Data registered at DCS
  • Local data collection



  • Sample Analysis
  • Borescopies
  • Thickness Measurement
  • Corrosion Coupons
  • Thermal Characterization
  • Hydraulic Characterization
  • Thermal Equivalent Model
  • Hydraulic Equivalent Model
  • Fouling mechanisms
  • Mechanisms

Simulation and Optimization of the Steam Cycle

  • Increase performance


Mechanical simulation

  • Integrity and Reliability
  • Operational limits
  • Accumulated Damage


Predictive Models

  • Integrity and Reliability
  • Operational limits




  • Sampling system
  • Dosing system
  • Water quality

Key points of Incycle’s analysis methodology for renewable steam cycles

  • Key points identification on-site for data gathering critical node.
  • Data evaluation in critical nodes to check its veracity and reliability.
  • Plant data collection in critical nodes.
  • Equipment characterization: based on actual plant data.
  • Generation of equivalent models (thermal, hydraulic and mechanical).
  • Integrated optimization of the cycle: performance improvement, reduction of mechanical damage and maintenance interventions. Increase in efficiency and service life.
  • Optimization of the monitoring system and the sampling of the water-steam system. Decision-making methodology based on reliable data
  • Equipment inspection and sample gathering. Dynamic models of corrosion and fouling.
  • Prediction of performance and needs of preventive maintenance.
  • Plant upgrades

Value proposition to Plant owners and O&M companies

  • Improvement of cycle performance, with a consequent efficiency increase 
  • Reduction of LCOE
  • Increase in equipment service life.
  • Reduction of maintenance shutdowns. Predictive Maintenance.
  • Improvement of the current monitoring system: Decision making based on true data

Further information:

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