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Microfine mineral particles have a small size, light weight, and low inertia, making it difficult for them to deviate from streamlines and collide with bubbles. Conventional flotation operations consume a large amount of reagents and exhibit poor flotation indicators. This study employs computational fluid dynamics (CFD) simulation and hydrodynamic testing to investigate the flow field within a high-turbulence microfine particle flotation machine equipped with a multilayer impeller–stator configuration, and validates the practical application performance of the microfine particle flotation machine through single-batch flotation experiments. Result shows that the impeller region of the traditional mechanical stirring flotation machine has a turbulent energy dissipation rate of 20 m²/s³, whereas that for the microfine particle flotation machine averages over 120 m²/s³. In the flotation verification, the cumulative recovery rate of the fine particle flotation machine is increased by 28% compared with that of the traditional KYF flotation machine. The flotation rate is also 1.3 times that of the KYF, demonstrating stronger selectivity for fine particle concentrates. It has certain guiding significance for the resource utilization of fine particle minerals. [ABSTRACT FROM AUTHOR]

The vehicle-routing problem (VRP) is a popular area of research. This popularity springs from its wide application in many real-world problems, such as logistics, network routing, E-commerce, and various other fields. The VRP is simple to formulate, but very difficult to solve and requires a great deal of time. In these cases, researchers use approximate solutions offered by metaheuristics. This work involved the design of a new metaheuristic called Open Competency Optimization (OCO), which was inspired by human behavior during the learning process and based on the competency approach. The aim is the construction of solutions that represent learners' ideas in the context of an open problem. The candidate solutions in OCO evolve over three steps. Concerning the first step, each learner builds a path of learning (finding the solution to the problem) through self-learning, which depends on their abilities. In the second step, each learner responds positively to the best ideas in their group (the construction of each group is based on the competency of the learners or the neighbor principle). In the last step, the learners interact with the best one in the group and with the leader. For the sake of proving the relevance of the proposed algorithm, OCO was tested in dynamic vehicle-routing problems along with the Generalized Dynamic Benchmark Generator (GDBG). [ABSTRACT FROM AUTHOR]

The rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in Thailand. By employing MARL, multiple autonomous agents learn to optimize charging strategies based on real-time data by adapting to fluctuating demand and varying electricity prices. Building upon previous research that applied MARL to static network configurations, this study extends the application to dynamic and real-world scenarios, integrating real-time data to refine agent learning processes and also evaluating the effectiveness of adaptive MARL in maximizing rewards and improving operational efficiency compared to traditional methods. Experimental results indicate that MARL-based strategies increased efficiency by 20% and reduced energy costs by 15% relative to conventional algorithms. Key findings demonstrate the potential of extending MARL in transforming EV charging network management, highlighting its benefits for stakeholders, including EV owners, operators, and utility providers. This research contributes insights into advancing electric mobility and energy management in Thailand through innovative AI-driven approaches. The implications of this study include significant improvements in the reliability and cost-effectiveness of EV charging networks, fostering greater adoption of electric vehicles and supporting sustainable energy initiatives. Future research directions include enhancing MARL adaptability and scalability as well as integrating predictive analytics for proactive network optimization and sustainability. These advancements promise to further refine the efficacy of EV charging networks, ensuring that they meet the growing demands of Thailand's evolving electric mobility landscape. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Economic analysis of catastrophic risk is a topic that unfortunately has become more relevant since the 1960s. An important question when a vital resource stock is at risk is whether one should invest more in the stock to create a buffer against a catastrophe or allow the stock to decrease as risk makes its future value decrease. The present paper analyses exogenous catastrophic risk where the probability of catastrophe cannot be controlled. Conditions for when precautionary behaviour is optimal are given with general functional forms. The paper analyses both problems with a single catastrophe and problems with an infinite sequence of catastrophes. It is shown that most of the results pertaining to a single catastrophe carry over to the case with an infinite number of catastrophes. [ABSTRACT FROM AUTHOR]

A multi-objective optimization is performed to obtain fueling conditions in hydrogen stations leading to improved filling times and thermodynamic efficiency (entropy production) of the de facto standard of operation, which is defined by the protocol SAE J2601. After finding the Pareto frontier between filling time and total entropy production, it was found that SAE J2601 is suboptimal in terms of these process variables. Specifically, reductions of filling time from 47 to 77% are possible in the analyzed range of ambient temperatures (from 10 to 40 °C) with higher saving potential the hotter the weather conditions. Maximum entropy production savings with respect to SAE J2601 (7% for 10 °C, 1% for 40 °C) demand a longer filling time that increases with ambient temperature (264% for 10 °C, 350% for 40 °C). Considering average electricity prices in California, USA, the operating cost of the filling process can be reduced between 8 and 28% without increasing the expected filling time. [ABSTRACT FROM AUTHOR]

We estimate the value of information to farmers from a soybean rust sentinel plot monitoring network. Monitoring provides value over time from farmers learning about their risk of infection and contemporaneously from farmers making more profitable fungicide applications. Optimizing the spatial configuration of the network reveals the value of information plateaus at around $920 million with around 75 sentinel plots. The optimal spatial distribution of sentinel plots differs substantially from historical placements. Optimal sentinel plot placement would have included fewer plots in the Southern United States, where the risk of infection is relatively high, and more in the Midwest, where soybean production is relatively high. [ABSTRACT FROM AUTHOR]

Dynamic resource allocation problems arise under a variety of settings. In "Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis," Balseiro, Besbes, and Pizarro introduce a unifying model for a large class of dynamic optimization problems dubbed dynamic resource-constrained reward collection (DRC2) problems. Surveying the literature, they show that this class encompasses a variety of disparate and classical problems typically studied separately, such as dynamic pricing with capacity constraints, dynamic bidding with budgets, network revenue management, online matching, or order fulfillment. Furthermore, they establish that the DRC2 class is amenable to analysis by characterizing the performance of a central, certainty-equivalent heuristic. Notably, they provide a novel unifying analysis that isolates the drivers of performance, recovers as corollaries some existing specialized results, generalizes other existing results by weakening the assumptions required, and yields new results in specialized settings for which no such characterization was available. Dynamic resource allocation problems arise under a variety of settings and have been studied across disciplines such as operations research and computer science. The present paper introduces a unifying model for a very large class of dynamic optimization problems that we call dynamic resource-constrained reward collection (DRC2) problems. We show that this class encompasses a variety of disparate and classical dynamic optimization problems such as dynamic pricing with capacity constraints, dynamic bidding with budgets, network revenue management, online matching, and order fulfillment, to name a few. Furthermore, we establish that the class of DRC2 problems, although highly general, is amenable to analysis. In particular, we characterize the performance of the fluid certainty-equivalent control heuristic for this class. Notably, this very general result recovers as corollaries some existing specialized results, generalizes other existing results by weakening the assumptions required, and also yields new results in specialized settings for which no such characterization was available. As such, the DRC2 class isolates some common features of a broad class of problems and offers a new object of analysis. Funding: The work of D. Pizarro was supported by the Artificial and Natural Intelligence Toulouse Institute, which is funded by the French "Investing for the Future—PIA3" program [Grant ANR-19-P3IA-0004]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2441. [ABSTRACT FROM AUTHOR]

The efficient operation and intelligent upgrading of public transportation can effectively enhance the attractiveness of conventional public transportation. In order to improve the delicacy management level of bus operations, this study designed a new dynamic optimization model for single-line bus operations with the dual optimization objectives of the lowest passenger travel cost and lowest operation cost, using a combination of the strategy of stop-skipping control and local route optimization. Simulated annealing (SA) was introduced into the genetic algorithm (GA) to design a hybrid heuristic algorithm for model solving. The effectiveness of the optimization model and the hybrid algorithm were verified and evaluated by using the No. 115 bus line in Ganzhou City as an example. The results showed that the proposed optimization model had a good usability, which can effectively improve the average vehicle speed, shorten the overall waiting time of passengers, and enhance the operational efficiency of the line. The hybrid algorithm saw significant improvement in terms of the iteration speed and the quality of the optimal solution compared with the conventional genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the need for innovative optimization solutions in automotive manufacturing. Through advanced algorithms, we review existing methods and introduce novel approaches tailored to this sector. Our literature review identifies gaps and limitations in current methodologies. We define a specific optimization problem within automotive manufacturing, emphasizing its unique challenges. Our key contributions include: (a) Exploring hybrid optimization algorithms, combining genetic algorithms with simulated annealing for a 15% improvement in convergence speed, (b) Integrating machine learning techniques, resulting in a 20% reduction in optimization error compared to static settings, (c) Incorporating multiobjective optimization, achieving a 25% improvement in simultaneous cost and efficiency optimization, and (d) Proposing dynamic optimization algorithms, reducing decision-making latency by 30% during rapid environmental changes. Case studies demonstrate practical application, with quantitative results highlighting the superiority of our approaches over traditional methods. Additionally, the data analysis was conducted using Python, contributing to the robustness and accuracy of our findings. [ABSTRACT FROM AUTHOR]

This study presents the first application of a logic‐based Benders decomposition (LBBD) technique in the field of simultaneous scheduling and dynamic optimization (SSDO), applied to network batch processes with a discrete‐time scheduling formulation. The proposed algorithm employs neighborhood information of ordered discrete decisions (e.g., batching variables) to generate cuts, rather than relying on traditional cut generation techniques based on dual information that are implemented in generalized Benders decomposition (GBD) algorithms. The proposed algorithm relies on solving multiple subproblems per iteration, which is a feature that allows the generation of multiple cuts per iteration thus producing accurate approximations of the objective function in shorter computational times. This results in the herein proposed multicut logic‐based discrete Benders decomposition (MLD‐BD) algorithm, which enables features such as a pruning strategy, and a cut‐off technique. Two case studies are used to demonstrate the computational advantages of the MLD‐BD framework against GBD and heuristic methodologies. [ABSTRACT FROM AUTHOR]

Due to their proteinaceous structure, monoclonal antibodies (mAbs) are susceptible to irreversible aggregation, with harmful consequences on drug efficacy and patient safety. To mitigate this risk in modern biopharmaceutical processes, it is critical to comply with current good manufacturing practices (cGMP) and pursue operating strategies minimizing irreversible aggregation whilst also maximizing mAb throughput. These conflicting objectives are targeted in this study by formulating and analyzing an integrated dynamic model accounting for both cultivation and aggregation of mAbs from a Chinese Hamster Ovary (CHO) cell line. Two manipulated dynamic variables are considered here in simulation studies: firstly temperature manipulation within a batch reactor, and secondly feed flow manipulation within a series of isothermal fed‐batch reactors. Following this, dynamic optimization investigations have been conducted, firstly with the single objective of maximizing mAb throughput and secondly with multiple (two) objectives of maximizing mAb throughput while also minimizing irreversible aggregate content, simultaneously. The study provides key insight into tradeoffs of how simultaneous temperature and feed flowrate manipulation affects mAb throughput and aggregation inside bioreactors. [ABSTRACT FROM AUTHOR]

The development of Chemical-looping Reforming (CLR) for blue hydrogen production requires a precise reactor design and control to achieve optimal efficiency and operability. This study proposes a 1D heterogeneous model for a CLR fixed-bed reactor, targeting the production of blue hydrogen and nitrogen. Validation against experimental data confirms the model's accuracy and reliability. A comprehensive sensitivity analysis highlights reactor variables, including active metal content in the oxygen carrier, steam-to-methane ratio, and molar flow rates during reduction and reforming stages, as key variables in determining reactor performance. Optimizing these variables is crucial for process intensification and enhancing hydrogen production efficiency. Under optimal conditions, the reactor yields nitrogen with a concentration above 98% during the oxidation stage and hydrogen with a 66.7% concentration during the reforming stage. Moreover, the CLR reactor operates autothermally and reaches a hydrogen production efficiency of 63.6%, prior to any further purification. The study concludes with a discussion of the reactor's challenges and opportunities, laying the groundwork for potential future research and advancements in the field. [Display omitted] • An intensified reactor for efficient blue H 2 and N 2 production is proposed. • Reactor key parameters are active metal, steam-to-methane ratio, and pressure. • Reactor optimization maximizes hydrogen production efficiency. • 66% H 2 yield in reforming and > 98 % N 2 purity in oxidation are achieved. [ABSTRACT FROM AUTHOR]

Rock and soil strength profoundly influences the stability of open-pit mine slopes. Traditional slope design, based on limited drilling data, often disregards inherent uncertainties. Effectively utilizing new sample information from mining operations poses a challenge, hindering dynamic and differentiated design for the entire perimeter slope. To address this, we propose a dynamic optimization method considering rock mass strength uncertainty for the entire perimeter slope. Our approach involves designing slope angles separately in different zones, while thoroughly considering decision-makers' preferences. Furthermore, we delegate the final adjustment authority of slope angles within the safety permissible range to on-site decision-makers. Compared to traditional methods, our dynamic design method incorporates rock mass strength uncertainty into slope evaluation while also accounting for decision-makers' safety and economic preferences. Through a case study of a specific open-pit mine, our proposed dynamic design method increases the overall slope angle by approximately 2.5°, fully accommodating the influence of on-site decision-making preferences on slope design. This article introduces a new method of dynamic optimization of open-pit mine slope based on simplified observation method, which improves the flexibility of decision-making and realizes the differential design of the whole surrounding slope. [ABSTRACT FROM AUTHOR]

Power-to-Ammonia (P2A) is a promising technology that can provide a low-emission energy carrier for long-term storage. This study presents an optimization approach to a novel small-scale containerized P2A concept commissioned in 2024. A dynamic nonlinear optimization problem of the P2A concept is set up, employing the non-commercial MOSAIC® software V3.0.1 in combination with the NEOS® server. In total, seven optimization solvers, ANTIGONE®, CONOPT®, IPOPT®, KNITRO®, MINOS®, PATHNLP®, and SNOPT®, are used. The first and main part of this work optimizes several disturbance scenarios of the concept and aims to determine the optimal reactor temperature profile to counter the disturbances. The optimization results suggest, for example, lowering the reactor temperature profile if the hydrogen and nitrogen inlet streams into the system decrease. The second part of this work presents a crude dynamic optimal scheduling model. This part aims to determine the amount of ammonia to be produced and sold given a randomized price of electricity for three consecutive points in time. The optimization results recommend decreasing production when the price of electricity is high and vice versa. However, the dynamic model must be improved to include fluctuations in the price of ammonia. Then, it can be used as a real-time optimization tool. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the application of intelligent education platforms has become increasingly widespread. Traditional teaching methods struggle to meet the demands for personalized learning. Personalized learning path decision-making methods, which analyze learners’ behavioral data and mastery of knowledge points, tailor learning paths for each individual. Current research indicates that these methods can significantly improve learning efficiency and effectiveness. However, existing personalized learning path decision-making methods still have shortcomings in predicting the difficulty of knowledge points and dynamically adjusting path recommendations. This paper proposes a machine learning-based personalized learning path decision-making method, focusing on two main aspects: predicting the difficulty of knowledge points for personalized learning and integrating knowledge point localization for personalized learning path decisions. Through accurate prediction of knowledge point difficulty and dynamically optimized learning path recommendations, this method provides more refined and personalized learning support on intelligent education platforms, aiming to enhance learners’ learning experience and outcomes. [ABSTRACT FROM AUTHOR]

This article presents an effective computational method based on the orthogonal collocation on finite element for nonlinear pursuit‐evasion differential game problems. The original problems are transformed into two dynamic optimization problems at first, so that the difficulty of obtaining the solution is reduced. To improve the convergence rate and the efficiency, the sensitivities describing the influence of control and interval parameters on state are derived through the discretized dynamic equations for the resulting nonlinear programming problem. The convergence speed is introduced to measure the performance in the upper level iteration. The main structure and the algorithm of the method are also given. Two demonstrative differential game problems with different scenarios from practice are studied. Compared with the approach without sensitivity information, the proposed method needs less function evaluations and saves at least 68.4% of the computational time. The research results show the effectiveness of proposed approach. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Solid-state fermentation (SSF) is the bioprocess where microorganisms are cultivated in the absence of free water under controlled conditions. Lactic acid can be produced by Rhizopus oryzae SSF of grape stalks. During the microorganism's growth, the temperature and water content of the solid bed fluctuate, leading to areas of either dry or excessive moisture in the solid substrate. Therefore, it is crucial to control the water supply to the matrix. In this work, we obtain lactic acid through SSF of grape stalks using Rhizopus oryzae NCIM 1299. The SSF was conducted at a fixed temperature of 35 °C, with five constant relative humidity (RH) levels: 50, 57, 65, 72, and 80%RH. Mathematical models, including the Logistic and First-Order Plus Dead-Time models for fungal biomass growth and the Luedeking and Piret with Delay Time model for lactic acid production, were adjusted to kinetic curves. Growth kinetic parameters (Xmax, μmax, Tp, T0, Yp/x, and td) were determined for all conditions. These kinetic parameters were then correlated with relative humidity using a second-degree polynomial relationship. We observed a decrease in Xmax with an increasing %RH, while the value of Yp/x increased at a higher %RH. Finally, the optimal variable relative humidity profile was obtained by applying the dynamic optimization technique, resulting in a 16.63% increase in lactic acid production. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic optimization method capable of optimizing the dynamic responses of a compliant parallel mechanism (CPM), in terms of its multiple primary resonant modes, is presented. A novel two-term objective function is formulated based on the specific frequency range and targeted ratios. The first term of the function is used to optimize the first resonant mode of the CPM, within a specific frequency range. The obtained frequency value of the first mode is used in the second term to define the remaining resonant modes to be optimized in terms of targeted ratios. Using the proposed objective function, the resonant modes of a CPM can be customized for a specific purpose, overcoming the limitations of existing methods. A 6-degree-of-freedom (DoF) CPM with decoupled motion is synthesized, monolithically prototyped, and investigated experimentally to demonstrate the effectiveness of the proposed function. The experimental results showed that the objective function is capable of optimizing the six resonant modes within the desired frequency range and the targeted ratios. The highest deviation between the experimental results and the predictions among the six resonant modes is found to be 9.42%, while the highest deviation in the compliances is 10.77%. The ranges of motions are found to be 10.0 mm in the translations, and 10.8° in the rotations. [ABSTRACT FROM AUTHOR]

Abstract Microfine mineral particles have a small size, light weight, and low inertia, making it difficult for them to deviate from streamlines and collide with bubbles. Conventional flotation operations consume a large amount of reagents and exhibit poor flotation indicators. This study employs computational fluid dynamics (CFD) simulation and hydrodynamic testing to investigate the flow field within a high-turbulence microfine particle flotation machine equipped with a multilayer impeller–stator configuration, and validates the practical application performance of the microfine particle flotation machine through single-batch flotation experiments. Result shows that the impeller region of the traditional mechanical stirring flotation machine has a turbulent energy dissipation rate of 20 m²/s³, whereas that for the microfine particle flotation machine averages over 120 m²/s³. In the flotation verification, the cumulative recovery rate of the fine particle flotation machine is increased by 28% compared with that of the traditional KYF flotation machine. The flotation rate is also 1.3 times that of the KYF, demonstrating stronger selectivity for fine particle concentrates. It has certain guiding significance for the resource utilization of fine particle minerals.

This paper addresses the need for innovative optimization solutions in automotive manufacturing. Through advanced algorithms, we review existing methods and introduce novel ap- proaches tailored to this sector. Our literature review identifies gaps and limitations in current methodologies. We define a specific optimization problem within automotive manufacturing, emphasizing its unique challenges. Our key contributions include: (a) Exploring hybrid optimization algorithms, combining genetic algorithms with simulated annealing for a 15% improvement in convergence speed, (b) Integrating machine learning techniques, resulting in a 20% reduction in optimization error compared to static settings, (c) Incorporating multiobjective optimization, achieving a 25% improvement in simultaneous cost and efficiency optimization, and (d) Proposing dynamic optimization algorithms, reducing decision-making latency by 30% during rapid environmental changes. Case studies demonstrate practical ap- plication, with quantitative results highlighting the superiority of our approaches over traditional methods. Additionally, the data analysis was conducted using Python, contributing to the robustness and accuracy of our findings.

Abstract Rock and soil strength profoundly influences the stability of open-pit mine slopes. Traditional slope design, based on limited drilling data, often disregards inherent uncertainties. Effectively utilizing new sample information from mining operations poses a challenge, hindering dynamic and differentiated design for the entire perimeter slope. To address this, we propose a dynamic optimization method considering rock mass strength uncertainty for the entire perimeter slope. Our approach involves designing slope angles separately in different zones, while thoroughly considering decision-makers' preferences. Furthermore, we delegate the final adjustment authority of slope angles within the safety permissible range to on-site decision-makers. Compared to traditional methods, our dynamic design method incorporates rock mass strength uncertainty into slope evaluation while also accounting for decision-makers' safety and economic preferences. Through a case study of a specific open-pit mine, our proposed dynamic design method increases the overall slope angle by approximately 2.5°, fully accommodating the influence of on-site decision-making preferences on slope design. This article introduces a new method of dynamic optimization of open-pit mine slope based on simplified observation method, which improves the flexibility of decision-making and realizes the differential design of the whole surrounding slope.

The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM). PCSCM is explicitly designed to leverage an enhanced memory system, capable of mitigating the issue of outdated particle memory after convergence, and efficiently adapting to changing environmental conditions. This innovative memory system retains and retrieves promising solutions from the past when environmental alterations occur. Additionally, PCSCM introduces clustering mechanisms for particles within each swarm, aimed at augmenting diversity within the problem space. This clustering strategy substantially bolsters the algorithm's performance in tracking evolving optimal solutions and positively contributes to its scalability. Crucially, the clustering approach is implemented not only for the main population but also for stored solutions in memory, which collectively strike a balance between exploration and exploitation. In the proposed method, particle swarms are divided into parent and child swarms, with parent swarms dedicated to preserving diversity; while, child swarms focus on identifying local solutions. These clustering and memory strategies are consistently applied within each sub-swarm to effectively address the challenges posed by high-dimensional search spaces. In addition to addressing challenges related to dynamic optimization, our proposed Parent–Child Multi-Swarm Clustered Memory (PCSCM) algorithm introduces an innovative mechanism for detecting environmental changes. This novel approach enhances the algorithm's adaptability by efficiently identifying moments when the optimization environment undergoes significant shifts. The detection of such changes is a crucial aspect of the PCSCM algorithm, contributing to its robust performance in dynamic scenarios. The effectiveness and robustness of the PCSCM algorithm are substantiated through extensive simulation experiments. These experiments provide insights into PCSCM's behavior in dynamic environments and showcase its ability to scale proficiently in high-dimensional settings. Particularly noteworthy are the results obtained when benchmarked against the Moving Peaks Benchmark and Generalized Moving Peaks Benchmark. These results not only underscore the algorithm's efficiency but also demonstrate its superiority when compared to several existing state-of-the-art optimization methods, including Multi-Swarm PSO, AmQSO, CPSO, Cellular PSO, FMSO, mQSO10 (5 + 5q), and DPSABC. [ABSTRACT FROM AUTHOR]

Setting a reasonable carbon reduction plan in coastal metropolises is the key part to reach the global carbon target. Carbon reduction will change urban climate and influence energy demand, both of which affect the optimization results of carbon reduction pathways. Current generation expansion optimization models consider direct abatement contribution and solve most problems of planning for long-term carbon emission reduction in energy systems. However, the construction of new type power systems also indirectly impacts carbon emissions by changing microclimate factors such as heat island intensity. By combining generation expansion with carbon emission prediction model, the proposed approach in this paper considers the hidden mechanism of carbon and heat emission change on air-conditioning loads and dynamically optimizes the carbon reduction pathways in coastal metropolises. Taking Pudong Area in Shanghai as an example, the estimated cost of carbon reduction is reduced by the proposed approach. Some suggestions for the carbon reduction in coastal metropolises are made according to the simulation results.

Vibrating flip-flow screens (VFFSs) provide an effective solution for deeply screening moist and fine-grained minerals, and an accurate dynamic model of VFFSs is critical for its dynamic analysis and optimization, thereby improving the vibration stability and symmetry of VFFSs. In this paper, uniaxial tension, uniaxial compression, plane tension, and shear stress relaxation experiments were conducted on screen panel samples to illustrate that the third-order Ogden model and the generalized Maxwell model can accurately describe the hyperelasticity and viscoelasticity of screen panels. Then, the coupling method of finite element and discrete element was adopted to establish the simulation model of the screen panel and material group coupling system, and the dynamics of the coupling system under different loading conditions were explored. Finally, the dynamic model of the coupling system of VFFSs mass, screen panel, and material group was proposed, and the non-dominated sorting genetic algorithm II was applied to optimize the system's dynamic response. The results reveal that the use of optimized shear springs can reduce the relative amplitude change rate of the main and floating screen frame by 44.30% while maintaining the periodic motion of the VFFSs under operation conditions, greatly enhancing the stability of the VFFSs system. [ABSTRACT FROM AUTHOR]

Civil engineering structures under ambient excitations are essentially time-varying and nonlinear structural systems and the resultant response signals exhibit non-stationarity. To reveal the time-varying characteristics of non-stationary signals, time frequency analysis methods with high resolutions are required. Although synchroextracting short time Fourier transform (SESTFT) is able to generate more energy concentrated time frequency representations and allow signal reconstruction, its major disadvantage is the window function is fixed. To address this issue, an enhanced synchroextracting wavelet transform (ESEWT) method is proposed to refine frequency bands by combing synchroextracting and continuous wavelet transform. After that, dynamic optimization (DO) is used to extract instantaneous frequency (IF) curves within the refined frequency bands. Two numerical examples and an experimental study case are investigated to illustrate the effectiveness and accuracy of the proposed method. The results demonstrate that the proposed ESEWT is capable of extracting frequency bands more accurately and its combination with DO identifies IFs of non-stationary signals better than current time frequency analysis methods such as SESTFT. [ABSTRACT FROM AUTHOR]

When solving the physical-control collaborative optimization problem, referred to as control co-design (CCD) problem, in dynamic system, it is inevitable to encounter the simulation model with computationally expensive state equation, leading to the inefficiency in extracting the Jacobian information of state equation and inefficiency of gradient-based CCD optimizer. Therefore, this work introduces an adaptive Kriging surrogate-based CCD method, in which the Jacobian information of state equation for the gradient-based optimizer is calculated by the time-saving Kriging model instead of the time-consuming original simulation model. More importantly, the feasibility of the surrogate-based CCD method is analyzed theoretically, and on the ground of the analysis, a novel adaptive sampling strategy based on distance clustering and maximum predictor errors, named DCMPE, is presented to assist the Kriging technique in approximating the state equation. Finally, three examples illustrate that the adaptive Kriging surrogate-based CCD method combined with DCMPE not only solves the CCD problems efficiently, but also provides a more accurate CCD solution with less computational effort compared to existing surrogate-based methods. [ABSTRACT FROM AUTHOR]

The dynamic pickup and delivery problem (DPDP) is essential in supply chain management and logistics. In this study, we consider a real-world DPDP from daily delivery scenarios of a company. In the problem, orders are generated randomly and released periodically. The orders should be completed as soon as possible to minimize the cost. We propose a novel memetic algorithm (MA) to address this problem. The proposed MA consists of a genetic algorithm and a local search strategy that periodically solves a static pickup and delivery problem when new orders are released. We have conducted extensive experiments on 64 real-world instances to assess the performance of our method. Three state-of-the-art algorithms are chosen as the baseline algorithms. Experimental results demonstrate the effectiveness of the MA in solving the real-world DPDP. [ABSTRACT FROM AUTHOR]

A decision maker repeatedly exerts efforts to produce new outputs, while being compensated based on his past and current production levels. We show that the decision maker's optimal strategy dictates a cyclic oscillatory performance whenever the compensation depends on recent past performance. We apply our model to various economic settings such as the delegated portfolio-managers problem, an R &D investment problem, and a dynamic advertising problem. [ABSTRACT FROM AUTHOR]

In the processing industry, dynamic product transitions are essential for achieving high product quality, minimizing the use of raw materials and energy and reducing production costs. However, optimizing dynamic product transitions is a challenging task due to the complex dynamics of the process and the uncertainty in the measurements. In this article, a data‐driven Bayesian approach for optimal dynamic product transitions is proposed. The proposed approach is based on a dynamic optimization problem that is solved using a Bayesian optimization algorithm. One of the advantages of this approach for process optimization tasks is that it does not require a first‐principles dynamic mathematical model for drawing optimal solutions. The approach is applied to three case studies, and the results are comparable in performance quality with those obtained using a traditional gradient‐based optimization approach. The results show that the proposed approach is able to find optimal transition trajectories that meet the product composition requirements using smooth control actions. The approach is also able to cope with noisy measurements, which is an important feature in real‐world applications. The proposed approach has several advantages over traditional optimization approaches, including being data driven, able to cope with noisy measurements, computationally efficient, and it requires modest computational effort. Complex online optimal control problems can benefit from adopting a data‐driven Bayesian optimization scheme. [ABSTRACT FROM AUTHOR]

For chemical processes, dynamic optimization is employed for process transition. On the basis of the multilayer control structure, the employment of dynamic optimization is affected by the regulatory control system. To avoid the adjustment of the regulatory control system, set-point optimization is proposed. For comparison, two types of optimization models, namely direct optimization and set-point optimization, are formulated. The superiority of set-point optimization is rigorously proven. By simulating the commercial process of a throughput-fluctuating ethylene column, the integrated absolute error and maximum deviation of product quality are reduced by more than 150% with set-point optimization. The results indicate that the approach to process transition via regulatory controllers not only avoids the insecurity caused by the switching of set-point controllers but also improves the optimization performance. In conclusion, the proposed optimization structure, namely set-point optimization, is operable and stable for commercial chemical process transitions. [ABSTRACT FROM AUTHOR]

Copyright of Chemical Engineering (China) / Huaxue Gongcheng is the property of Hualu Engineering Science & Technology Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Middle vessel batch distillation (MVBD) is an energy-efficient configuration for separation of a ternary mixture. This paper focuses on improving the performance of this configuration through dynamic optimization of vessel holdup. Initially, a performance measure accounting for separation and energy efficiency is defined to characterize an operational policy. Subsequently, this measure is maximized by dynamically redistributing holdup in the three (top, middle and bottom) vessels. With the help of two case studies, the impact of various policy decisions and market conditions (such as initial feed distribution and relative cost of products and energy) on the optimal operating policy is analyzed. Specifically, the improvement obtained via holdup redistribution is explained with the help of fundamental concepts of distillation. Lastly, the performance of the proposed approach is compared with some of the existing methods and validated through rigorous simulations.

ABSTRACTGrain legume production offers multiple environmental benefits and can enhance the sustainability of farming, but the legume area has been small and declining over the last decades in most European countries. Recently, grain legumes have gained importance because of the increases in prices of feed and food proteins, fertilizers and fuel, in addition to sustainability concerns. This study investigated the impacts of introducing grain legumes [faba beans (Vicia faba L.)] in cereal-dominated crop production systems typical for southwestern Finland. To investigate the economic effects as well as the effect grain legumes have on production and land use a dynamic optimization model was used. The results suggest significant crop yield gains if farmers consistently utilize the pre-crop value of legumes and other crops in crop rotations over several years. The farm economic gains of diversified legume rotations were found to be positive but relatively small assuming past prices, but they can be significantly higher if legume and nitrogen fertilizer prices increase in the future. Overall, faba bean-based rotations have positive long-term implications on soil quality and biodiversity and thus future viability and societal reputation of farming.

• Pricing mechanism of battery recycling e-platforms is clarified. • Non-equalizing supply and demand are considered. • Platform revenue is more sensitive to commission rates than social welfare. • Social welfare is more susceptible to recycling prices. • Higher recycling volume implies a greater supply–demand imbalance. With the rapid advancement of electric vehicles (EVs), the burgeoning increase in used power batteries necessitates the development of efficient battery recycling e-platforms. A key challenge in this field is the mismatch between supply and demand. In response, a dynamic optimization model is proposed to capture the non-equalizing supply–demand relationship and its linkage over continuous periods to enable dynamic simulations and predictions of transaction volume changes. Meanwhile, pricing and commission-setting strategies are optimized based on the objectives of maximizing social welfare and platform revenue. The result shows that due to the lower recycling volumes that result, increasing the recycling price usually increases platform revenues, exacerbates environmental costs, and leads to lower social welfare. Moreover, platform revenues are more sensitive to commission rates than social welfare, which is more vulnerable to recycling prices. Furthermore, prioritizing social welfare leads to a higher recycling volume compared to prioritizing revenue, but it also creates an imbalance between supply and demand, destabilizing the recycling market. With the dynamic pricing and commission strategies, this study enriches the literature in the third-party recycling mode for power batteries, offering a novel perspective that is more aligned with real-world operational conditions. Our findings help platforms clarify the impact of pricing and commission decisions on platform revenue and social welfare and thereby provide support for their decision optimization. [ABSTRACT FROM AUTHOR]

Nonpharmaceutical interventions (NPI) are an important tool for countering pandemics such as COVID-19. Some are cheap; others disrupt economic, educational, and social activity. The latter force governments to balance the health benefits of reduced infection and death against broader lockdown-induced societal costs. A literature has developed modeling how to optimally adjust lockdown intensity as an epidemic evolves. This paper extends that literature by augmenting the classic SIR model with additional states and flows capturing decay over time in vaccine-conferred immunity, the possibility that mutations create variants that erode immunity, and that protection against infection erodes faster than protecting against severe illness. As in past models, we find that small changes in parameter values can tip the optimal response between very different solutions, but the extensions considered here create new types of solutions. In some instances, it can be optimal to incur perpetual epidemic waves even if the uncontrolled infection prevalence would settle down to a stable intermediate level. [ABSTRACT FROM AUTHOR]

The paper proposes a new model-based optimization approach to improve the clinical efficiency of compensatory insulin bolus treatment in diabetic patients, aiming to mitigate the consequences of diabetes. The most important contribution of this paper is a novel methodology for determining the optimal parameters of insulin treatment, namely the size and timing of insulin boluses, to effectively compensate for carbohydrate intake. This concept can be seen as the so-called optimal model-based bolus calculator. The presented theoretical framework deals with the problem of optimal disturbance rejection in impulsive systems by minimizing an integral quadratic cost function. The methodology considers a personalized empirical transfer function model with static gains and time constants as the only parameters assumed to be known, making the bolus calculator more straightforward to implement in clinical practice. Contrary to other techniques, the proposed methodology considers impulsive insulin administration in the form of boluses, which is more feasible than continuous infusion. In contrast to the conventional bolus calculator, the proposed algorithm allows for maximizing therapy performance by optimizing the relative time of insulin bolus administration with respect to carbohydrate intake. Another feature to highlight is that the solution of the optimization problem can be obtained analytically, hence no numerical iterative solvers are required. Additionally, the continuous-time domain approach allows for a much finer adjustments of the insulin administration timing compared to discrete-time models. The proposed approach was validated in an in-silico study, which demonstrated the importance of systematically determined insulin–carbohydrate ratio and the relative delay between disturbance and its compensation. The results showed that the proposed optimal bolus calculator outperforms the traditional suboptimal formula. [ABSTRACT FROM AUTHOR]

Many real-world problems are dynamic optimization problems that are unknown beforehand. In practice, unpredictable events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in parameters or constraints make the search environment dynamic. Many algorithms are designed to deal with stationary optimization problems, but these algorithms do not face dynamic optimization problems or manage them correctly. Although some optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima. With this in mind, we studied a variant of SSA known as QSSO, which integrates the principles of quantum computing. An attempt is made to improve the overall performance of standard SSA to deal with the dynamic environment effectively by locating and tracking the global optima for DOPs. This work is an extension of the proposed new algorithm QSSO, known as the Quantum-inspired Chaotic Salp Swarm Optimization (QCSSO) Algorithm, which details the various approaches considered while solving DOPs. A chaotic operator is employed with quantum computing to respond to change and guarantee to increase individual searchability by improving population diversity and the speed at which the algorithm converges. We experimented by evaluating QCSSO on a well-known generalized dynamic benchmark problem (GDBG) provided for CEC 2009, followed by a comparative numerical study with well-regarded algorithms. As promised, the introduced QCSSO is discovered as the rival algorithm for DOPs. [ABSTRACT FROM AUTHOR]