Multiobjective Programming for Waste Heat Recovery of an Industrial Gas Turbine

Multiobjective Programming for Waste Heat Recovery of an Industrial Gas Turbine

DOI: 10.4018/978-1-7998-1710-9.ch003
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In gas power plants, the overall efficiency of the generation system plays a key role in ensuring stable and efficient power supply. Terms and conditions of power supply are usually detailed in power purchase agreements (PPA). Current requirements set by PPAs limit the net power produced by the supplier. This creates opportunities for plant optimization efforts to focus on system efficiency—aiming to increase system lifetime with lower operational costs. In this chapter, a gas turbine (GT) system is considered to demonstrate certain features of power plant optimization. Waste heat from the GT exhaust stack is fed into an absorption chiller (AC). The AC cools the air intake at the GT compressor. This cooling reduces the heat rate and increases the GT efficiency. This combined GT-AC system was optimized in a multi-objective (MO) setting while considering power limitations (imposed by the PPA).
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Part 1: Optimization Of The Three-Objective Gt-Ac System Without Uncertainty In Weather Conditions


In most power production industries, the overall efficiency of the generation system plays a key role. Optimizing the system increases its efficiency resulting in higher power production. Most power producers are bound contractually by PPAs which details the amount of power they are allowed to produce (Wu and Babich, 2012; Cory et al., 2009). The strategies involved in drawing up the contract vary from country to country and represent an alignment towards local policies and scenarios. Some of these contracts require that the amount of power supplied remain fixed throughout the period of purchase. Nevertheless, there may be requirements specified in the contracts to combine fixed generation with additional available power. This is done to address concerns over unplanned outages. For contracts (PPA) involving fixed power supply: although engineers and plant personnel manage to optimize the plant efficiency significantly, the surplus power produced could not be sold. The motivation to pursue power generation efficiency in this sense can also relate to savings in fuel consumption - without the need to increase the power supplied. This work aims to optimize the design and operation parameters for the combined Gas Turbine (GT) and Absorption Chiller (AC) system without maximizing the total power supplied to the consumer. The optimization model for the combined system would hence contain multiple objectives (which represent key indicators of efficiency) while constraining the GT power output such that it adheres to PPA requirements.

By optimizing the design and operations of the combined GT-AC system, the cost efficiency of the plant would significantly increase. Additionally, this optimization would also enable the GT to operate at maximum efficiency as per design. This would contribute to the increase of its active lifetime. Recovering the waste heat works in favor of the environment since the plant does not dispose large amounts of heat to its surrounding.

Real-world engineering applications often present scenarios with multiple target objectives. Such classes of problems are often difficult to solve. Even if solved, these solutions are difficult to analyze due to their multidimensional and complex nature. However, as industrial systems become more complex, engineers and decision makers often find themselves in situations involving multiple objectives (Ganesan et al., 2014; Ganesan et al., 2015). The idea of Pareto-optimality is prevalent when it comes to tracing-out the non-dominated solution options at the Pareto curve (Deb et al., 2002). An alternative to non-dominated solution tracing is the function aggregation method – where multiple objective functions are merged into a single master function effectively transforming the problem into a weighted single-objective problem (Marler and Arora, 2010; Brito et al., 2014; Naidu et al., 2014). Detail examples and analyses on MO techniques for problems in engineering optimization are presented in Oliveira and Saramago, (2010) and Rao and Rao, (2009).

MO scenarios have been encountered in power generation and aerospace - especially when dealing with gas turbines (GT). For instance, in García-Revillo et al., (2014), a Multiobjective Genetic Algorithm (MOGA) was employed for optimizing the geometry of aeronautical GT discs. In that work the authors considered fatigue life prediction and total geometrical mass as objective functions. In Yazdi, et al., (2015), the modeling and optimization of a micro turbine cycle was done – where the design parameters; compression ratio, compressor isentropic efficiency, combustion chamber inlet temperature and turbine inlet temperature was considered. The authors considered power exergy efficiency, total cost and carbon dioxide emission of the plant as the three objectives to be optimized. Khorasani Nejad et al., (2013) successfully optimized the total cost rate and power cycle efficiency of a GT power plant coupled with an AC system (which cools the compressor inlet air). The authors employed a Multiobjective Genetic Algorithm as the optimization technique. For accurately describing a combined system encompassing a GT, heat recovery steam generator and a LiBr-based AC, a multiobjective evaluation index (MEI) model was developed by Sun et al., (2014). The proposed model was then utilized to optimize the combined system. A similar MO combined system was modeled in Ahmadi et al., (2013). Their system consisted of a micro gas turbine, boiler, AC, ejector refrigeration cycle, domestic water heater and a proton-exchange membrane electrolyzer. The system in Ahmadi et al., (2013) was aimed to produce power, heating, cooling, hot water and hydrogen. Using a non-dominated sorting genetic algorithm (NSGA-II), the authors optimized the combined system focusing on two objectives; total cost rate and system’s exergy efficiency.

Evolutionary algorithms have proven to be highly successful when used for solving problems in engineering optimization. Among the most successful evolutionary techniques are differential evolution (DE) (Price et al.,2006), genetic algorithms (Grefenstette, 2013), evolutionary strategy (Beyer, 2013) and genetic programming (Shao et al., 2014). DE is a population-based evolutionary algorithm that has been derived from genetic algorithms. Developed in the nineties, DE has been successfully applied to engineering problems which are non-differentiable, non-continuous, non-linear, noisy, and multidimensional. These problems often contain many local minima, constraints and have a high degree of stochasticity. Lately, DE has been applied to a variety of areas including optimization problems in energy systems (Rout, 2013; Mohanty et al., 2014; Debbarma et al., 2014). In this work the basic DE technique is employed in conjunction to the weighted sum approach (Liu et al., 2011). To obtain a solution closer to the global optima, the random generator of the conventional DE technique is enhanced with a chaotic component. As previously mentioned solution evaluation for MO problems could prove difficult. Therefore in this work the Hypervolume Indicator (HVI) is employed for this purpose (Zitzler et al.,2007). The HVI is a set measure reflecting the volume enclosed by a Pareto front approximation and a reference set (Emmerich et al., 2005; Jiang et al., 2015).

This primary focus of this chapter is the novel modeling of a real-world combined GT and AC multicriteria optimization problem. Key concepts of the model as well as the solution method presented in this chapter can be found in the author’s original past works (Ganesan et al., 2016; Ganesan et al., 2018). This model is optimized by maximizing the thermal efficiency of the GT system and the coefficient of performance (COP) of the AC. Optimizing the fuel consumption, the fuel cost is minimized while maintaining constant power supply to respect the PPA with the consumer. To obtain optimal parameters, two evolutionary-type algorithms are employed: DE and the improved CDDE. Optimization results are discussed and analyzed in detail in Part 1 of this chapter.

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