Particle swarm optimization of thermal enhanced oil recovery from oilfields with temperature control

Siavashi, Majid; Doranehgard, Mohammad Hossein

doi:10.1016/j.applthermaleng.2017.05.109

Cited by 55 publications

(8 citation statements)

References 62 publications

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“…It combines two methodologies: artificial life and evolutionary computation [53]. Based on this algorithm, a group of particles is distributed in the N-dimensional space that N shows the number of variables, which must be optimized [54]. Each particle in the search space maintains the position, velocity, and individual best position.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

et al. 2020

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In this paper, a new hybrid model is developed to improve the accuracy in the prediction of the longitudinal dispersion coefficient (Kx) and the derivation of novel optimized explicit equations for natural streams. For this purpose, an artificial neural network (ANN) is hybridized with particle swarm optimization (PSO) and cat swarm optimization (CSO) algorithms. The CSO and PSO are used to find the optimum values of biases and weights in ANN structure and formulate the results as novel explicit predictive equations than the classical black-box methods. The hydraulic parameters of the natural stream and some geometric parameters were utilized for the model developments. Eight different input combinations are used as the input vectors to ANN, ANN-PSO, and ANN-CSO models, whereas the dispersion coefficient (Kx) is the target model output. The developed models are trained and tested bya comprehensive reference data sets measured on streams in the United States, that were used previously by Tayfur and Singh (2005) in ANN models. The main aims, novelty, and contributions of the present study are 1) improving the accuracy of classical ANN-based Kx predictions by hybridizing with CSO and PSO. 2) Performing sensitive analysis of ANN, ANN-CSO, and ANN-PSO based on input combinations 3) derivation of novel explicit optimized ANN-CSO, ANN-PSO, equations for predicting Kx rather than the classical ANN black-box methods. The results depicted that the highest accuracy and superiority were attained by the ANN-PSO model, with input variables of B, H, U, U*, followed by ANN-CSO and ANN. By using the optimized trained black box ANN models, two novel explicit predictive equations are derived, and their results are compared with the empirical equations. Comparative assessments confirmed significant improvements in the hybrid equations' results than the classical ANN and previously published equations. The developed novel equations can be used to estimate the Kx in one-dimensional pollutant transfer models that are essential for the pollution studies in environmental river engineering practices.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

et al. 2020

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“…Kennedy and Eberhart introduced Particle swarm optimization (PSO) (Du and Swamy, 2016), involving simulating behaviors to find the most suitable results. Literature exposes that several PSO optimization strategies in task scheduling (Jamali et al, 2016;Prathibha et al, 2017), medical (Jothi, 2016;Ryalat et al, 2016), oil and gas (Salehi and Goorkani, 2017;Siavashi and Doranehgard, 2017), batik production (Soesanti and Syahputra, 2016) have been positively applied in biochemical processes because of their controlled parameters to solve optimization problems (Liu et al, 2008).…”

Section: Fermentation Strategiesmentioning

confidence: 99%

Bacterial Pigments: Sustainable Compounds With Market Potential for Pharma and Food Industry

Venil

Dufossé

Devi

2020

Front. Sustain. Food Syst.

106

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The continued universal application of synthetic colorants for decades have caused environmental pollutions and human health vulnerabilities. So, it was indispensable to discover novel natural colorants such as microbial colorants which were safer and better than synthetic colorants. The potential of bacterial pigments for mass production of diversified coloring properties was first prospective and is now getting the notable importance and attention of both the researchers and industries. Literature establishes that the natural colorants produced from microbes were applied in food and pharma products successfully. Apart from serving as food colorants, bacterial pigments have several pharmacological activities like anti-microbial, anti-cancer, anti-oxidant, anti-inflammatory and anti-allergic properties with large economic potential. And, there is vast scope for easy and cheap production of natural colorants in all seasons from bacterial sources, compared to plant sources. Tactics in strain improvement, fermentation conditions, metabolic engineering, and easy extraction techniques are needed to produce high end products. This review highlights the significance of bacterial colorants and summarizes its application in food and pharma industries. Further, the major challenge of lower stability of bacterial pigments and the solution to address it is also appraised.

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“…In Fig. 3 Model estimation and forecasting performance of the Bayesian model and linear model without the Bayesian approach recent years, PSO has been used in well placement and field development applications and has given better results compared with other optimization algorithms [37][38][39][40]. The PSO searching method is presented in the Appendix section.…”

Section: Optimizer Input Parametersmentioning

confidence: 99%

Comparative study of different risk measures for robust optimization of oil production under the market uncertainty: a regret-based insight

2020

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Model-based production optimization relies on a dynamic model that simulates the fluid flow in the oil reservoirs, and an economic objective function that assigns an economic measure to the recoverable oil reserves. An optimization algorithm utilizes the dynamic model to find the production scenario which maximizes the economic measure of profit. However, due to incompleteness and doubtfulness of available data, the reservoir model describing the complex subsurface geology is quite uncertain. Moreover, the definition of the economic objective functions such as net present value (NPV) requires economic variables such as oil price, interest rate, and production costs which unpredictably vary with time. In recent years, robust optimization (RO) has been widely used as an appropriate tool for handling the uncertainties in production optimization problems. However, previous works on robust optimization paid less attention to economic uncertainties arising from market volatility. Instead, they are mostly focused on geological uncertainties. This paper is devoted to production optimization under oil market uncertainty. To narrow down the range of economic uncertainties, a Bayesian framework for oil price history matching and forecasting has been developed which allows generating more reliable realizations of oil price future trend. It is common to include a measure of risk-averse in the objective function of RO problems. However, the quality of the solutions depends directly on the used risk measure. In the oil industry, risk measures such as worst-case scenario and CVaR (Conditional Value at Risk) have been used to mitigate the risk of low-profit realizations. These risk measures are appropriate in many cases for measuring the robustness. Though, they are inadequate in evaluating robustness in a relative sense in cases where the worst-case realizations have an undue effect on the final decisions. The risk measure defined based on the minimax regret approach takes into account all realizations instead of just considering the worst-case realizations. In this research, RO has been performed to maximize NPV using the minimax regret approach. In addition, the results are compared with the common risk measures used in the oil industry including expected profit, CVaR, and worst-case. Results show that while worstcase scenario and CVaR perform better than other risk measures in lower-profit realizations, they give inappropriate results for other scenarios. In contrast, regret-based approach and expected profit give nearly optimum solutions for all realizations. In this paper, the minimax regret approach was compared with other risk measures in the presence of oil price uncertainty. However, the results might be extended to optimization under geological uncertainty.

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Particle swarm optimization of thermal enhanced oil recovery from oilfields with temperature control

Cited by 55 publications

References 62 publications

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

Bacterial Pigments: Sustainable Compounds With Market Potential for Pharma and Food Industry

Comparative study of different risk measures for robust optimization of oil production under the market uncertainty: a regret-based insight

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