Global polystyrene (PS) production has been influenced by the lightness and heat resistance this material offers in different applications, such as construction and packaging. However, population growth and the lack of PS recycling lead to a large waste generation, affecting the environment. Pyrolysis has been recognized as an effective recycling method, converting PS waste into valuable products in the chemical industry. The present work addresses a systematic, bibliometric, and statistical analysis of results carried out from 2015 to 2022, making an extensive critique of the most influential operation parameters in the thermo-catalytic pyrolysis of PS and its waste. The systematic study showed that the conversion of PS into a liquid with high aromatic content (84.75% of styrene) can be achieved by pyrolysis. Discussion of PS as fuel is described compared to commercial fuels. In addition, PS favors the production of liquid fuel when subjected to co-pyrolysis with biomass, improving its properties such as viscosity and energy content. A statistical analysis of the data compilation was also discussed, evaluating the influence of temperature, reactor design, and catalysts on product yield.

We address the important step of determining an effective subset of heuristics in selection hyper-heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an unnecessary large pool can decrease the performance of adaptive approaches. Our goal is to bring methodological rigour to this step. The proposed methodology uses non-parametric statistics and fitness landscape measurements from an available set of heuristics and benchmark instances, in order to produce a compact subset of effective heuristics for the underlying problem. We also propose a new iterated local search hyper-heuristic usingmulti-armed banditscoupled with a change detection mechanism. The methodology is tested on two real-world optimisation problems: course timetabling and vehicle routing. The proposed hyper-heuristic with a compact heuristic pool, outperforms state-of-the-art hyper-heuristics and competes with problem-specific methods in course timetabling, even producing new best-known solutions in 5 out of the 24 studied instances

This paper presents a grammatical evolution (GE)-based methodology to automatically design third generation artificial neural networks (ANNs), also known as spiking neural networks (SNNs), for solving supervised classification problems. The proposal performs the SNN design by exploring the search space of three-layered feedforward topologies with configured synaptic connections (weights and delays) so that no explicit training is carried out. Besides, the designed SNNs have partial connections between input and hidden layers which may contribute to avoid redundancies and reduce the dimensionality of input feature vectors. The proposal was tested on several well-known benchmark datasets from the UCI repository and statistically compared against a similar design methodology for second generation ANNs and an adapted version of that methodology for SNNs; also, the results of the two methodologies and the proposed one were improved by changing the fitness function in the design process. The proposed methodology shows competitive and consistent results, and the statistical tests support the conclusion that the designs produced by the proposal perform better than those produced by other methodologies.