The IRC-PD Tool: A Code to Design Steam and Organic Waste Heat Recovery Units

Abstract

The Algerian economy and electricity generation sector are strongly dependent on fossil fuels. Over 93% of Algerian exports are hydrocarbons, and approximately 90% of the generated electricity comes from natural gas power plants. However, Algeria is also a country with huge potential in terms of both renewable energy sources and industrial processes waste heat recovery. For these reasons, the government launched an ambitious program to foster renewable energy sources and industrial energy efficiency. In this context, steam and organic Rankine cycles could play a crucial role; however, there is a need for reliable and time-efficient optimization tools that take into account technical, economic, environmental, and safety aspects. For this purpose, the authors built a mathematical tool able to optimize both steam and organic Rankine units. The tool, called Improved Rankine Cycle Plant Designer, was developed in MATLAB environment, uses the Genetic Algorithm toolbox, acquires the fluids thermophysical properties from CoolProp and REFPROP databases, while the safety information is derived from the ASHRAE database. The tool, designed to support the development of both RES and industrial processes waste heat recovery, could perform single or multi-objective optimizations of the steam Rankine cycle layout and of a multiple set of organic Rankine cycle configurations, including the ones which adopt a water or an oil thermal loop. In the case of the ORC unit, the working fluid is selected among more than 120 pure fluids and their mixtures. The turbines’ design parameters and the adoption of a water- or an air-cooled condenser are also optimization results. To facilitate the plant layout and working fluid selection, the economic analysis is performed to better evaluate the plant economic feasibility after the thermodynamic optimization of the cycle. Considering the willingness of moving from a fossil to a RES-based economy, there is a need for adopting plants using low environmental impact working fluids. However, because ORC fluids are subjected to environmental and safety issues, as well as phase out, the code also computes the Total Equivalent Warming Impact, provides safety information using the ASHRAE database, and displays an alert if the organic substance is phased out or is going to be banned. To show the tool’s potentialities and improve the knowledge on waste heat recovery in bio-gas plants, the authors selected an in-operation facility in which the waste heat is released by a 1 MWel internal combustion engine as the test case. The optimization outcomes reveal that the technical, economic, environmental, and safety performance can be achieved adopting the organic Rankine cycle recuperative configuration. The unit, which adopts Benzene as working fluid, needs to be decoupled from the heat source by means of an oil thermal loop. This optimized solution guarantees to boost the electricity production of the bio-gas facility up to 15%.

Special Issue “Industry and Tertiary Sectors towards Clean Energy Transition”

“…In terms of specific technologies, those analysed by the contributions in this Special Issue mainly refer to energy use and electricity generation, namely waste-heat recovery [6,7], electricity storage and renewable electricity production [8,9] covering both energy efficiency and decarbonisation dimensions.…”

“…In this context, reliable and time-efficient optimisation tools are needed, considering technical, economic, environmental and safety aspects. Redjeb et al [7] built a mathematical tool capable of optimising both steam and organic Rankine units. The tool could perform single or multi-objective optimisations of the steam Rankine cycle layout and of a multiple set of organic Rankine cycle configurations.…”

Multi-Objective Optimization of Organic Rankine Cycle (ORC) for Tractor Waste Heat Recovery Based on Particle Swarm Optimization

“…Additionally, the PSO was used to optimize the net output power as the objective function, and results revealed that the overall thermal and exergy efficiencies were obtained at 18.23% and 62.37%, respectively, for the base case [18]. A reliable and time-efficient optimization tool was developed in the MATLAB environment that was able to optimize ORC, the genetic algorithm (GA) toolbox was used, and the fluids' thermophysical properties were acquired from the CoolProp [19] and REFPROP [20] databases [21]. A machine learning prediction model was developed and applied to predict the ORC power output, GA was used to optimize the initial weights and thresholds of the different structural parameter ORC model to further improve the model generalization ability [22].…”