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GEOENERGY SCIENCE AND ENGINEERING 2023年 223卷

作者： Hatamizadeh, Abdollah Sedaee, Behnam Univ Tehran Inst Petr Engn Coll Engn Chem Engn Dept Tehran Iran

Optimizing the process of acidizing in carbonate reservoirs is essential to increase the effectiveness of permeability improvement, reduce the pressure drop of near-well bore, and minimize acid consumption. Experimental studies of carbonate reservoirs acidizing and its optimization are time-consuming and expensive. Moreover, numerical simulation of this process, which has been introduced as an alternative to experimental studies, is not practical at industrial scales due to the considerable computation time and the increasing complexities of acidizing itself. Machine learning (ML), neural networks, and meta-learning algorithms can act as a facilitator in simulating and optimizing this complicated process. Previous studies have not used machine learning and metalearning algorithm to simulate multi-phase carbonate reservoirs acidizing and genetic algorithms for optimizing their operational parameters. In this study, multiphase carbonate reservoirs acidizing is simulated at core scale and validated using published experimental data Because of employing actual data and the nature of artificial intelligence approaches, it is feasible to model the complexities of acidizing in carbonate reservoirs and have acceptable results. Porosity and permeability and their distribution, core dimensions, fluids saturation, oil viscosity, oil density, oil compressibility, injection rate, temperature, pressure, acid concentration, acid molecular diffusion, and reaction rate are the effective parameters in this procedure whose objective function is the acid pore volume to breakthrough (PVBT). After gathering available valid data from the literature, various machine learning methods were employed to model this process. However, due to the problem's complexities, typical machine learning and neural networks methods would not provide the desired accuracy, and more recent approaches such as meta-learning are required to attain reasonable precision. Having modeled the acidizing of carbonate re

关键词： Carbonate reservoirs acidizing Meta -learning Machine learning optimization Genetic algorithm

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CATO: End-to-End optimization of ML-Based Traffic Analysis Pipelines

arXiv

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arXiv 2024年

作者： Wan, Gerry Liu, Shinan Bronzino, Francesco Feamster, Nick Durumeric, Zakir Stanford University United States University of Chicago United States École Normale Supérieure de Lyon France

Machine learning has shown tremendous potential for improving the capabilities of network traffic analysis applications, often outperforming simpler rule-based heuristics. However, ML-based solutions remain difficult to deploy in practice. Many existing approaches only optimize the predictive performance of their models, overlooking the practical challenges of running them against network traffic in real time. This is especially problematic in the domain of traffic analysis, where the efficiency of the serving pipeline is a critical factor in determining the usability of a model. In this work, we introduce CATO, a framework that addresses this problem by jointly optimizing the predictive performance and the associated systems costs of the serving pipeline. CATO leverages recent advances in multi-objective Bayesian optimization to efficiently identify Pareto-optimal configurations, and automatically compiles end-to-end optimized serving pipelines that can be deployed in real networks. Our evaluations show that compared to popular feature optimization techniques, CATO can provide up to 3600× lower inference latency and 3.7× higher zero-loss throughput while simultaneously achieving better model performance. © 2024, CC BY.

关键词： Pareto principle

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Representation Online Matters: practical End-to-End Diversification in Search and Recommender Systems 23

Representation Online Matters: Practical End-to-End Diversif...

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6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023

作者： Silva, Pedro Juneja, Bhawna Desai, Shloka Singh, Ashudeep Fawaz, Nadia Pinterest Inc. San FranciscoCA United States

ISBN: (纸本)9781450372527

As the use of online platforms continues to grow across all demographics, users often express a desire to feel represented in the content. To improve representation in search results and recommendations, we introduce end-to-end diversification, ensuring that diverse content flows throughout the various stages of these systems, from retrieval to ranking. We develop, experiment, and deploy scalable diversification mechanisms in multiple production surfaces on the Pinterest platform, including Search, Related Products, and New User Homefeed, to improve the representation of different skin tones in beauty and fashion content. Diversification in production systems includes three components: identifying requests that will trigger diversification, ensuring diverse content is retrieved from the large content corpus during the retrieval stage, and finally, balancing the diversity-utility trade-off in a self-adjusting manner in the ranking stage. Our approaches, which evolved from using Strong-OR logical operator to bucketized retrieval at the retrieval stage and from greedy re-rankers to multi-objective optimization using determinantal point processes for the ranking stage, balances diversity and utility while enabling fast iterations and scalable expansion to diversification over multiple dimensions. Our experiments indicate that these approaches significantly improve diversity metrics, with a neutral to a positive impact on utility metrics and improved user satisfaction, both qualitatively and quantitatively, in production. © 2023 Owner/Author.

关键词： Recommender systems

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A differentiable structural analysis framework for high-performance design optimization

arXiv

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arXiv 2024年

作者： Lee, Keith J. Huang, Yijiang Mueller, Caitlin T. Massachusetts Institute of Technology Cambridge United States Zürich Switzerland

Fast, gradient-based structural optimization has long been limited to a highly restricted subset of problems-namely, density-based compliance minimization-for which gradients can be analytically derived. For other objective functions, constraints, and design parameterizations, computing gradients has remained inaccessible, requiring the use of derivative-free algorithms that scale poorly with problem size. This has restricted the applicability of optimization to abstracted and academic problems, and has limited the uptake of these potentially impactful methods in practice. In this paper, we bridge the gap between computational efficiency and the freedom of problem formulation through a differentiable analysis framework designed for general structural optimization. We achieve this through leveraging Automatic Differentiation (AD) to manage the complex computational graph of structural analysis programs, and implementing specific derivation rules for performance critical functions along this graph. This paper provides a complete overview of gradient computation for arbitrary structural design objectives, identifies the barriers to their practical use, and derives key intermediate derivative operations that resolves these bottlenecks. Our framework is then tested against a series of structural design problems of increasing complexity: two highly constrained minimum volume problem, a multi-stage shape and section design problem, and an embodied carbon minimization problem. We benchmark our framework against other common optimization approaches, and show that our method outperforms others in terms of speed, stability, and solution quality. © 2024, CC BY.

关键词： Structural optimization

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Towards Principled, practical Policy Gradient for Bandits and Tabular MDPs

arXiv

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arXiv 2024年

作者： Lu, Michael Aghaei, Matin Raj, Anant Vaswani, Sharan Simon Fraser University Canada

We consider (stochastic) softmax policy gradient (PG) methods for bandits and tabular Markov decision processes (MDPs). While the PG objective is non-concave, recent research has used the objective's smoothness and gradient domination properties to achieve convergence to an optimal policy. However, these theoretical results require setting the algorithm parameters according to unknown problem-dependent quantities (e.g. the optimal action or the true reward vector in a bandit problem). To address this issue, we borrow ideas from the optimization literature to design practical, principled PG methods in both the exact and stochastic settings. In the exact setting, we employ an Armijo line-search to set the step-size for softmax PG and demonstrate a linear convergence rate. In the stochastic setting, we utilize exponentially decreasing step-sizes, and characterize the convergence rate of the resulting algorithm. We show that the proposed algorithm offers similar theoretical guarantees as the state-of-the art results, but does not require the knowledge of oracle-like quantities. For the multi-armed bandit setting, our techniques result in a theoretically-principled PG algorithm that does not require explicit exploration, the knowledge of the reward gap, the reward distributions, or the noise. Finally, we empirically compare the proposed methods to PG approaches that require oracle knowledge, and demonstrate competitive performance. © 2024, CC BY.

关键词： Stochastic systems

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MOSHEPO: a hybrid multi-objective approach to solve economic load dispatch and micro grid problems

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APPLIED INTELLIGENCE 2020年第1期50卷 119-137页

作者： Dhiman, Gaurav Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala 147004 Punjab India Govt Bikram Coll Commerce Dept Comp Sci Patiala 147004 Punjab India

This paper proposes a novel hybrid multi-objective algorithm named multi-objective Spotted Hyena and Emperor Penguin Optimizer (MOSHEPO) for solving both convex and non-convex economic dispatch and micro grid power dispatch problems. The proposed algorithm combines two newly developed bio-inspired optimization algorithms namely multi-objective Spotted Hyena Optimizer (MOSHO) and Emperor Penguin Optimizer (EPO). MOSHEPO contemplates many non-linear characteristics of power generators such as transmission losses, multiple fuels, valve-point loading, and prohibited operating zones along with their operational constraints, for practical operation. To evaluate the effectiveness of MOSHEPO, the proposed algorithm has been tested on various benchmark test systems and its performance is compared with other well-known approaches. The experimental results demonstrate that the proposed algorithm outperforms other algorithms with low computational efforts while solving economic and micro grid power dispatch problems.

关键词： multi-objective optimization Spotted hyena optimizer Emperor penguin optimizer Economic dispatch Micro grid

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Investor Preference-Aware Portfolio optimization with Deep Reinforcement Learning

Investor Preference-Aware Portfolio Optimization with Deep R...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Li, Zhenglong Tam, Vincent Yeung, Kwan L. Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

ISBN: (纸本)9798331521165

Recently, deep reinforcement learning (RL) approaches have been adopted to optimize financial portfolios with the objective of maximizing total profits while reducing potential risks by spreading investment capital across a variety of assets. Despite achieving great advances in the trade-off between profits and risks, the existing deep RL-based frameworks rarely consider practical trading constraints when making decisions in real-world financial markets, which cannot fulfill the customized requirements of specific users and may violate market regulations. Accordingly, a multi-Agent and Self-Adaptive trading framework for Constrained portfolio optimization, namely the MASAC, is proposed in which the deep RL-based agent dynamically explores profit-maximization policies while the heuristic-based agent conducts min-conflict search to ensure the generated trading strategies satisfying all concerned constraints. Through sharing knowledge within the trading system, the agents of the proposed framework cooperatively produce new portfolios that can maximize overall profits while satisfying all investor requirements throughout the trading period. The experimental results reveal the advantages of the MASAC framework against many state-of-the-art approaches in investment performance and trading constraint satisfaction on the challenging data sets of real-world markets. © 2025 IEEE.

关键词： Risk management

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Adjoint-Based Sensitivity and optimization of Turbulent Reacting Flows

Adjoint-Based Sensitivity and Optimization of Turbulent Reac...

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作者： Kord, Ali University of Michigan

学位级别：Ph.D., Doctor of Philosophy

Turbulent reacting flows drive many energy conversion devices and play crucial roles in the power generation and transportation sectors. Due to their chaotic and multi-scale nature, predicting and optimizing such systems is challenging. Over the past several decades, direct numerical simulation (DNS) and large-eddy simulation (LES) have gained in popularity within the scientific and engineering community for simulating this class of flows. However, owing to their high computational cost, they have primarily been used to investigate micro-scale physics or develop sub-grid scale models. Meanwhile, optimizing new engineering systems or improving existing devices requires iterating upon many design/input parameters. A brute-force trial-and-error approach involves performing many simulations and is thus not practical even with modern computational resources. Current approaches typically reduce the complexity of the model, which compromises its fidelity and decreases dimensionality of the physical system. Discrete adjoint-based methods provide exact sensitivity of a quantity of interest (QoI) to many input parameters with a tractable computational cost. The sensitivity gradient obtained from an adjoint solution provides a direction to adjust parameters for minimizing (i.e., improving) the QoI. However, computing discrete adjoint sensitivity from high-fidelity numerical simulations like DNS or LES is challenging. Modern numerical methods are typically developed for solving the original governing equations and are not necessarily consistent with the discrete adjoint formulation. The objective of this dissertation is to develop a high-fidelity numerical framework that provides exact sensitivity of a QoI for turbulent reacting flows. This builds off state-of-the-art numerical discretization methods and extends them to be compatible with a discrete adjoint solver. The adjoint sensitivity is combined with gradient-based optimization techniques to find optimal parameters. The nu

关键词： Discrete adjoint-based sensitivity and optimization High-fidelity simulations Turbulent reacting flows SBP-SAT framework Scalar boundedness via adaptive artificial dissipation Flamelet/progress variable approach

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Thermodynamic and thermoeconomic analysis and optimization of a renewable-based hybrid system for power, hydrogen, and freshwater production

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ENERGY 2024年 295卷

作者： Gao, Jinling Zhang, Yong Li, Xuetao Zhou, Xiao Kilburn, Zofia J. Hubei Univ Automot Technol Sch Econ & Management Shiyan 442000 Hubei Peoples R China Jiaying Univ Meizhou 514015 Guangdong Peoples R China Hubei Univ Automot Technol Sch Math Phys & Optoelect Engn Shiyan 442000 Hubei Peoples R China Tallinn Univ Technol Dept Mech & Ind Engn Ehitajate Tee EE-512616 Tallinn Estonia

To address environmental pollution effectively, it is crucial to promote the increased utilization of renewable energy sources. Furthermore, an appealing opportunity arises from enhancing the efficiency of renewable-based power plants while diversifying their product output. This study introduces a hybrid system that revolves around renewable resources, with a primary focus on evaluating power generation, hydrogen production, and freshwater extraction. The designed system seamlessly integrates a flash-binary geothermal power plant with a solar system, incorporating cutting-edge phase-change material storage technology. Hydrogen is generated through a combination of steam-methanol reforming and pressure swing adsorption processes. Freshwater is procured utilizing humidification-dehumidification and multi-effect desalination units. In terms of power generation, the system leverages the capabilities of the geothermal power plant alongside a modified Kalina cycle. The performance of this integrated system is rigorously evaluated through a combination of thermodynamic and thermoeconomic approaches. An exergy-economic optimization scenario is employed to determine the most efficient operational mode. The results of this comprehensive analysis reveal that the system can produce 0.0224 kg/s of hydrogen, 8.017 kg/s of freshwater, and generate 215.9 W of net power. Impressively, it achieves an exergy efficiency of 58.3% at a unit cost of $32.23/GJ in the base mode. Furthermore, the optimal operating state boosts efficiency to 60.59%, with a unit cost of $32.22/GJ. Notably, adjustments in the selling price of hydrogen have a significant impact on the system's financial metrics. As the price of hydrogen rises from $5 to $6/kg, the payback period reduces from 4 to 3 years, and the net present value surges from $5.81 million to $10.16 million.

关键词： Renewable -based hybrid system Steam -methanol reforming Phase -change material Thermoeconomic analysis multi -objective optimization

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multi-objectiveoptimization of solid oxide fuel cell/gas turbine combined heat and power system: A comparison between particle swarm and genetic algorithms

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2020年第11期44卷 9001-9020页

作者： Safari, Sadegh Hajilounezhad, Taher Ehyaei, Mehdi Ali Islamic Azad Univ Fac Nat Resources & Environm Sci & Res Branch Dept Energy Engn Tehran Iran Univ Missouri Dept Mech & Aerosp Engn Columbia MO 65211 USA Islamic Azad Univ Pardis Branch Dept Mech Engn Pardis Iran

Many studies have attempted to optimize integrated Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT), although different and somehow conflicting results are reported employing various algorithms. In this study, multi-objective optimization (MOO) is employed to approach the optimal design of SOFC-GT considering all prevailing factors. The emphasis is placed on the evaluation of the Particle Swarm optimization (PSO) and Genetic Algorithm (GA) performance as two effective approaches for solving the multi-objective and non-linear optimization problems. multi- objective optimization is carried out on two vital objectives;the electrical efficiency and the overall output power of the system. The considerable achievements are the set of optimal points that aim to identify the system optimal performance which provides a practical basis for the decision-makers to choose the appropriate target functions. For the studied conditions, the two algorithms nearly exhibit similar performance, while the PSO is faster and more efficient in terms of computational effort. The PSO appears to achieve its ultimate parameter values in fewer generations compared to the GA algorithm under the examined circumstances. It is found that the maximum power of 410 kW is accomplished employing the GA optimization method with an efficiency of 64%, while PSO method yields the maximum power of 419.19 kW at the efficiency of 58.9%. The results stress that PSO offers more satisfactory convergence and fidelity of the solution for the SOFC-GT MOO problems.

关键词： gas turbine heat recovery steam generation modeling multi-objective optimization solid oxide fuel cell

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