The Physics
The Solution
There has always been a problem with thermodynamic systems in that they are both non-linear and not reversible. This has limited the mathematical analysis that can be done on real plants. The exergy optimiser solves these two problems and provides a fully mathematically defined system that can be analysed by conventional min/max analysis of a well formed set of partial differential equations.
What does that mean in simple terms. The system which is patent protected, allows you to mathematically optimise a system ( that is work out the most efficient operating point) using physics and mathematics to get to a real optimum, not one that is assumed to be optimum because it has used prior experience or data mining and statistical probabilities to get to a place that is though to be (but is never proven definitively to be) an optimum.
Exergy uses the fact that the system will have losses no matter how good the optimum operating condition is, and it uses these losses to define the optimum point, that is the minimum losses define the most efficient operating point as minimum losses by definition implies maximum output.
To control any system requires measured parameters to know what is happening and it requires the ability to change these measured parameters to adjust the operating point. One of the major benefits of the exergy optimiser is that it uses the normal parameters measured during operation of generation plant to both analyse the system and to adjust the operating point. Temperature, pressure and mass flows are used in standard thermodynamics to generate the operating model which is then transformed into a Reimannian metric for analysis.
Using the Reimannian mass energy space, and introducing Reimannian curvature tensors increases the number of variables and equations to provide a solvable set of partial differential equations. Solving these equations for the point of minimum losses using standard mathematical processes yields the optimum operating point. Christoffel connections are then used to map the optimum back to the optimum Galilean operating point.
This analysis is made possible by modelling the losses mathematically in a way that makes the analysis reversible in the patent protected process.
What does this mean for a plant operator? He can take the normal controllable operating parameters and feed these to the optimiser. The optimiser makes the necessary calculations and reports back the values the operating variables should be set to for the plant to produce maximum output for the input energy, or the calculation can adjust the operating point to the least fuel use for a set output.
Second Law Efficiency
Consider Niagara Falls as an example. The water at the top of the falls has gravitational potential energy by virtue of its height above the gorge. This energy is transformed into kinetic energy as the water drops over the falls. The kinetic energy of the falling water can be used to produce work, and indeed is used very successfully to generate electricity.
But what about the water at the bottom of the falls. It is tempting to believe that the original gravitational potential energy that the water possessed at the top of the falls has been lost. "Has the first law been violated? No, the water at the bottom of the falls is one eighth of a Celsius degree warmer than the water at the top of the Falls." This heat energy is caused by the friction of the water molecules crashing against one another and against the rocks. The roar of the falls indicates that some of the original gravitational potential energy has also been transformed into sound energy.
The total amount of sound and heat energy at the bottom of the falls is exactly equal to the total amount of gravitational potential energy at the top of the falls. The essential difference between the two forms of energy is that we can harness the high quality concentrated gravitational potential energy at the top of the falls to perform work -- we can run an electric generator for example -- whereas the low quality heat and sound energy at the bottom is too dispersed to be of any use.
The quest for consistent and meaningful efficiency parameters The main concern of performance or efficiency engineers is with the fuel cost. Because a power plant is rather a complex system constituting hundreds of components and processes, one attempts to define various efficiencies in order to monitor the performance of different types of machinery. These ad-hoc efficiencies are not yielded by physics in any unique manner, but rather are designed to satisfy basic intuitions associated with conservation of energy. Hence are arbitrary and in general inconsistent with each other. The efficiency parameter of interest to managers is the Unit Heat-Rate (or Specific Fuel Consumption); at a given load it encompasses the fuel charges up to a factor. It is desirable that all other efficiencies defined throughout the plant will be consistent with the heatrate as well as amongst themselves. This can be achieved by replacing intuition and ad-hoc consideration with the constraints imposed on power-plant operation(s) by the Second Law of Thermodynamics.
Second Law Efficiency
The ratio of the minimum amount of work or energy required to perform a task to the amount actually used.
The additively of entropy enables an Exergy Audit of the power Plant Since Entropy is an extensive property it is additive; space wise it means that the sum of the entropies of constituent components equals the entropy of the combined system.
Entropy (Exergy) is not a conserved quantity Time-wise entropy is not a conserved quantity: the entropy of an isolated thermodynamic system either increases with time, or remains constant, as the system evolves. In the latter case we say that system evolves reversibly.
Unit heatrate trend correlates exactly with Exergy loss trend This is so because the individual dissipations or Exergy loss equals exactly the heat flux into the sink due to irreversibility's the said processes. Any increase in those yields extra fuel to maintain the load, hence worse unit heatrate.
These 3 properties yield an algorithm for tracking down quantitatively the root cause of heatrate excursion
Such pinpointing is otherwise an extremely time-consuming and daunting task, if at all possible; there certainly does not exist an algorithmic procedure to track down root-cause to heatrate excursions without invoking entropy.
The Opportunity
When the early versions of the system were being tested operators were used to adjust the inputs to overcome the lag between measurement and results. Because the calculations are efficient and modern computers are faster the original trial to run the system as a closed loop was underway when Dr Yasni unexpectedly passed away. The tests were not completed, but were described by him as producing sensible results.
The system was established as being capable of producing a true physics based digital twin of a power generation system that could be used to control the system to run at its optimum operation point in real time. This is the real value in this invention.
A side benefit is that the underlying performance monitoring that is incorporated in the system tracks entropy through the plant and can identify areas where losses are penalising the plant performance and can report these both by process location to and magnitude. This allows preventive and anticipatory maintenance to be planned into operating schedules and allows (if the operator chooses) real benefit /cost analysis of maintenance works.
There are also opportunities to develop the system as a universal simulator and to incorporate it into real time preventive maintenance and condition monitoring system that will combine mechanical and electrical monitoring with plant degradation that is not normally detectable with these systems.