AGUILON is a powerful software platform which consists of various components which starts from measuring the process parameters, generating valuable insights from the data and finally to recommend the optimized parameters to achieve best plant efficiency. It continuously stores the data of all the plant equipment’s in real time and makes the data available for monitoring, billing, analysis, predictive analytics and prescriptive analytics. Intelligent algorithms and functionalities assess process conditions in terms of performances monitoring, analysis, and diagnose of relevant equipment process data.
Complete Plant Accounting & Settlement System for Power & Water Plants. Accounting & Settlement system is an important functionality for a plant which exports power and water.
PPM solution is the blend of AGUILON products which takes care from monitoring the data sources to providing valuable insights on the asset across the fleet and enterprise.
Recommends the optimum operational strategies and set points to enhance the operational efficiency, reduce emissions, cost of generation and to improve the equipment life.
Fuel Demand Model is a thermodynamic model-based system for combined water and power plant which predicts the oil/gas demand based on the existing load and environmental conditions. It combines the complete thermodynamic model of the power and desalination plant.
Energy industries are in a competitive environment needs to address the requirement of energy demand and in parallel need to deliver it with optimized cost by keeping the environmental parameters within control by following government policies and regulations. Since energy industries are subjected to thermal and mechanical environments, the equipment’s are easily prone to the degradation over a period of time. Hence the degradation also drags the performance of the equipment. keeping all these constraints within its limits, energy utilities needs to deliver its output smartly. Hence Predictive & Prescriptive analytics plays a vital role in detecting the degradation and recommending the optimized set point.