Quantum-Inspired Genetic Programming Algorithm for the Crude Oil Scheduling of a Real-World Refinery

Pereira, Cristiane Salgado; Dias, Douglas Mota; Pacheco, Marco Aurélio Cavalcanti; Vellasco, Marley; Cruz, André Vargas Abs da; Hollmann, Estefane Horn

doi:10.1109/jsyst.2020.2968039

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…Implementing a genetic algorithm requires carefully considering several critical parameters that collectively shape the algorithm's overall performance. These parameters include population size, crossover rate, generation interval, number of generations, convergence of the evaluation function, number of rounds, and seeding rate [53,54].…”

Section: Evolution Parametersmentioning

confidence: 99%

“…Population size, representing the number of individuals in a population, directly influences the diversity and exploration capacity of the algorithm. The crossover rate, determining the probability of two individuals undergoing crossover, balances exploration and exploitation [53,54]. The generation interval, closely tied to population size, defines the percentage of the population replaced in each new generation.…”

Section: Evolution Parametersmentioning

confidence: 99%

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Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

de Oliveira,

Brandt,

Salvadori

et al. 2024

Inventions

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Photovoltaic (PV) systems face challenges in achieving maximum energy extraction due to the non-linear nature of their current versus voltage (IxV) characteristics, which are influenced by temperature and solar irradiation. These factors lead to variations in power generation. The situation becomes even more complex under partial shading conditions, causing distortion in the characteristic curve and creating discrepancies between local and global maximum power points. Achieving the highest output is crucial to enhancing energy efficiency in such systems. However, conventional maximum power point tracking (MPPT) techniques often struggle to locate the global maximum point required to extract the maximum power from the PV system. This study employs genetic algorithms (GAs) to address this issue. The system can efficiently search for the global maximum point using genetic algorithms, maximizing power extraction from the PV arrangements. The proposed approach is compared with the traditional Perturb and Observe (P&O) method through simulations, demonstrating its superior effectiveness in achieving optimal power generation.

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Section: Evolution Parametersmentioning

confidence: 99%

Section: Evolution Parametersmentioning

confidence: 99%

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

de Oliveira,

Brandt,

Salvadori

et al. 2024

Inventions

View full text Add to dashboard Cite

show abstract

“…Considering this objective, a biobjective optimization model can be established. (20) Equation 20 indicates that the larger the objective function value, the more useful work the equipment set does and the better the energy efficiency indicators and the easier it is to save. In the actual production process, enterprises tend to shorten the processing time of idle machines to improve production efficiency, which also means reducing the useless work of the equipment set, consistent with the objective proposed in this paper.…”

Section: Establishment Of Steelmaking Energy Efficiency Scheduling Modelmentioning

confidence: 99%

“…Panda and Ramteke 19 proposed a structure-adapted genetic algorithm to prevent crude oil scheduling under demand uncertainty. Pereira 20 proposed a new algorithm that integrates linear and grammar-guided genetic programming concepts with a quantum-inspired approach to create programs that represent a crude oil refinery scheduling solution. Hou et al 21 solved the problem of processing both low-fusion-point oil (L-oil) and high-fusion-point oil (H-oil) with an adaptive enhanced selection pressure algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Steelmaking Energy Efficiency Scheduling Based on an Equipment Set Shutdown Strategy

Wang,

Liu,

Chen

et al. 2023

ACS Omega

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The steel industry accounts for a large proportion of power consumption in industries. To greatly reduce the power consumption of production, it is urgent to adjust and optimize the steelmaking production mode. The paper combines production scheduling with equipment energy efficiency indicators, establishing an optimization model for steelmaking energy efficiency scheduling and determining the shutdown strategy of steelmaking equipment sets. Taking two equipment sets of a company processing the same batch of steel as an example, this paper calculates that the unit energy consumption under the optimal scheduling scheme is 79.492 and 22.056 kWh, respectively. The energy consumption of the former to complete the production task is greater than that of the latter. Therefore, by choosing to shut down this equipment set, a total of 65 038.2 kWh of electricity can be saved. Industrial examples were executed to validate the effectiveness of the model, and the results showed that the proposed method can obtain optimal solutions in a short period of time and significantly reduce energy consumption in the workshop. This study first combines scheduling issues with equipment energy efficiency indicators to provide a basis for energy consumption decisions.

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A brief review on quantum computing based drug design

Das,

Ray,

Bhattacharyya

et al. 2024

WIREs Data Min & Knowl

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Design and development of new drug molecules are essential for the survival of human society. New drugs are designed for therapeutic purposes to combat new diseases. Besides treating new diseases, new drug development is also needed to treat pre‐existing diseases more effectively and reduce the existing drugs' side effects. The design of drugs involves several steps, from the discovery of the drug molecule to its commercialization in the market. One of the most critical steps in drug design is to find the molecular interactions between the target (infected) molecule and the drug molecule. Several complex chemical equations need to be solved to determine the molecular interactions. In the late 20th Century, the advancement of computational technologies has made the solution of chemical equations relatively easier and faster. Moreover, the design of drug molecules involves multi‐criteria optimization. Classical computational methodologies have been used for drug design since the end of the 20th Century. However, nowadays, more advanced computational methodologies are inevitable in designing drugs for new diseases and drugs with fewer side effects. In this context, the quantum computing paradigm has proved beneficial in drug design due to its advanced computational capabilities. This paper presents a state‐of‐the‐art comprehensive review of the quantum computing‐based methodologies involved in drug design. A comparative study is made about the different quantum‐aided drug design methods, stating each methodology's merits and demerits. The review work presented in this manuscript will help new researchers assess the present state‐of‐the‐art concept of quantum‐based drug design.This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Computational Intelligence Application Areas > Health Care

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Quantum-Inspired Genetic Programming Algorithm for the Crude Oil Scheduling of a Real-World Refinery

Cited by 8 publications

References 17 publications

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

Optimization of Steelmaking Energy Efficiency Scheduling Based on an Equipment Set Shutdown Strategy

A brief review on quantum computing based drug design

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