Multiobjective Programming for Waste Heat Recovery of an Industrial Gas Turbine

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).

Computational Intelligence in Energy Generation

As economies become increasingly complex, so do their associated energy generation systems. Therefore, engineers and decision makers in this sector are spurred to seek out state-of-the-art approaches to deal with this rapid increase in system complexity. An effective strategy to deal with this scenario is to employ computational intelligence (CI) methods. CI supplements the heuristics used by the engineer—enhancing the cumulative analytic capacity to effectively resolve complicated scenarios. CI could be split to two classes: predictive modeling and optimization. In this chapter, past applications of CI in energy generation are discussed. The sectors presented here are renewable energy systems, distributed generation, nuclear power plants, coal power, and gas-fueled plants.

Biofuel Supply Chain Optimization Using Lévy-Enhanced Swarm Intelligence

Supply chain planning/optimization presents various challenges to decision makers globally due to its highly complicated nature as well as its large-scale structure. Over the years various state-of-the-art methods have been employed to model supply chains. Optimization techniques are then applied to such models to help with optimal decision making. However, with highly complex industrial systems such as these, conventional metaheuristics are still plagued by various drawbacks. Strategies such as hybridization and algorithmic modifications have been the focus of previous efforts to improve the performance of conventional metaheuristics. In light of these developments, this chapter presents two solution methods for tackling the biofuel supply chain problem.

Optimization of a Solar-powered Irrigation System

Optimization is now a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors when their system interacts with the environment (e.g., solar insolation and ambient temperature fluctuations). In this chapter, the sizing and design optimization of the solar powered irrigation system is considered. This problem is multivariate, noisy, nonlinear, and multiobjective (MO). This chapter is divided into two parts where two situations are considered during the optimization of the solar powered irrigation system. Part 1 is the MO design optimization of the mentioned system under constant weather conditions. Part 2 involves optimizing a more general form of the design problem by accounting for varying weather conditions, insolation, and ambient temperature. The details of the optimization procedures of the two cases are presented and discussed in this chapter.

Biofuel Supply Chain Optimization Using Random Matrix Generators

Supply chain problems are large-scale problems with complex interlinked variables. This sort of characteristic closely resembles structures often encountered in the nuclei of heavy atoms (e.g., platinum, gold or rhodium). Such structures are said to have the property of universality.

Multiobjective Optimization for Waste Heat Recovery of an Industrial Gas Turbine using Extreme Value Stochastic Engines

Stochastic engines or random number generators are commonly used to initialize metaheuristic approaches. This chapter discusses the incorporation of extreme value distributions into stochastic engines to improve the performance of optimization techniques when solving the complex four-objective GT-AC optimization problem.