• Proceedings of the SAREK Conference
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• 2005.11a
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• pp.463-468
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• 2005

An experimental study on the optimal control algorithm for central heating system for minimizing energy consumption while maintaining the comfort of indoor thermal en vironment in terms of the environmental variables such as loads and weather. experimental study has been done by one using the prototype of central heating system. As a result the optimal control algorithm shows good energy performance in comparison with conventional control one.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1471-1480
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• 2020

This research presents a case study on the remediation of a radioactive waste (uranium: U) utilizing a multi-objective fuzzy optimization in an electrocoagulation process for the iron-stainless steel and aluminum-stainless steel anode/cathode systems. The incorporation of the cumulative uncertainty of result, operational cost and energy consumption are essential key elements in determining the feasibility of the developed model equations in satisfying specific maximum contaminant level (MCL) required by stringent environmental regulations worldwide. Pareto-optimal solutions showed that the iron system (0 ㎍/L U: 492 USD/g-U) outperformed the aluminum system (96 ㎍/L U: 747 USD/g-U) in terms of the retained uranium concentration and energy consumption. Thus, the iron system was further carried out in a multi-objective analysis due to its feasibility in satisfying various uranium standard regulatory limits. Based on the 30 ㎍/L MCL, the decision-making process via fuzzy logic showed an overall satisfaction of 6.1% at a treatment time and current density of 101.6 min and 59.9 mA/㎠, respectively. The fuzzy optimal solution reveals the following: uranium concentration - 5 ㎍/L, cumulative uncertainty - 25 ㎍/L, energy consumption - 461.7 kWh/g-U and operational cost based on electricity cost in the United States - 60.0 USD/g-U, South Korea - 55.4 USD/g-U and Finland - 78.5 USD/g-U.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.1
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• pp.29-38
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• 2024

Wastewater management is increasingly emphasizing economic and environmental sustainability. Traditional methods in sewage treatment plants have significant implications for the environment and the economy due to power and chemical consumption, and sludge generation. To address these challenges, a study was conducted to develop the Intermittent Cycle Extended Aeration System (ICEAS). This approach was implemented as the primary technique in a full-scale wastewater treatment facility, utilizing key operational factors within the standard Sequencing Batch Reactor (SBR) process. The optimal operational approach, identified in this study, was put into practice at the research facility from January 2020 to December 2022. By implementing management strategies within the biological reactor, it was shown that maintaining and reducing chemical quantities, sludge generation, power consumption, and related costs could yield economic benefits. Moreover, adapting operations to influent characteristics and seasonal conditions allowed for efficient blower operation, reducing unnecessary electricity consumption and ensuring proper dissolved oxygen levels. Despite annual increases in influent flow rate and concentration, this study demonstrated the ability to maintain and reduce sludge production, electricity consumption, and chemical usage. Additionally, systematic responses to emergencies and abnormal situations significantly contributed to economic, technical, and environmental benefits.

• Journal of the Chungcheong Mathematical Society
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• v.32 no.2
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• pp.215-224
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• 2019

We investigate the optimal consumption and investment decision problem of an agent whose time preference is time-inconsistent. Specifically, for a time-separable utility function, the agent's subjective discount factor is supposed to be changed randomly in the future. We provide closed-form solutions in the presence of income process. The method can be extended into the case with a stochastic income process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4260-4266
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• 2015

In this study, the optimal control method for minimizing of energy consumption for central cooling system with proper occupant comfort level is researched by simulation. The optimal control method is that the optimal set temperatures such as the condenser water temperature, supply air temperature, and chilled water temperature with environment variable change such as outdoor air dry-bulb and wet-bulb temperatures are obtained by suggested optimal control algorithm with maximum and part building load. The TRNSYS program is used for system modeling and the control performances with the suggested optimal control method are compared with the existing control method of fixed set points. The suggested optimal control method shows better responses in energy consumption in comparison with existing control ones.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.2
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• pp.277-281
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• 2012

I study an optimal consumption and portfolio selection problem with regime-switching using a dynamic programming method. With constant relative risk aversion (CRRA) utility I obtain optimal solutions in closed-form.

• Journal of Communications and Networks
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• v.17 no.2
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• pp.184-197
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• 2015

This paper builds on a recent method, chain routing with even energy consumption (CREEC), for designing a wireless sensor network with chain topology and for scheduling the communication to ensure even average energy consumption in the network. In here a new suboptimal design is proposed and compared with the CREEC design. The chain topology in CREEC is reconfigured after each group of n converge-casts with the goal of making the energy consumption along the new paths between the nodes in the chain as even as possible. The new method described in this paper designs a single near-optimal Hamiltonian circuit, used to obtain multiple chains having only the terminal nodes different at different converge-casts. The advantage of the new scheme is that for the whole life of the network most of the communication takes place between same pairs of nodes, therefore keeping topology reconfigurations at a minimum. The optimal scheduling of the communication between the network and base station in order to maximize network lifetime, given the chosen minimum length circuit, becomes a simple linear programming problem which needs to be solved only once, at the initialization stage. The maximum lifetime obtained when using any combination of chains is shown to be upper bounded by the solution of a suitable linear programming problem. The upper bounds show that the proposed method provides near-optimal solutions for several wireless sensor network parameter sets.

• Transactions of Materials Processing
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• v.32 no.3
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• pp.153-159
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• 2023

This paper presents the optimal design of a heating system using radiant heating elements for application in smart farms. Smart farming, an advanced agricultural technology, is based on artificial intelligence and the internet of things and promotes crop production. Temperature and humidity regulation is critical in smart farms, and thus, a heating system is essential. Radiant heating elements are devices that generate heat using electrical energy. Among other applications, radiant heating elements are used for environmental control and heating in smart farm greenhouses. The performance of these elements is directly related to their electrical energy consumption. Therefore, achieving a balance between efficient electrical energy consumption and maximum heating performance in smart farms is crucial for the optimal design of radiant heating elements. In this study, the size, electrical energy supply, heat generation efficiency, and heating performance of radiant heating elements used in these heating systems were investigated. The effects of the size and electrical energy supply of radiant heating elements on the heating performance were experimentally analyzed. As the radiant heating element size increased, the heat generation efficiency improved, but the electrical energy consumption also increased. In addition, increasing the electrical energy supply improved both the heat generation efficiency and heating performance of the radiant heating elements. Based on these results, a method for determining the optimal size and electrical energy supply of radiant heating elements was proposed, and it reduced the electrical energy consumption while maintaining an appropriate heating performance in smart farms. These research findings are expected to contribute to energy conservation and performance improvement in smart farming.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.36-44
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• 2014

Exploiting the energy-delay tradeoff for energy saving is critical for developing green wireless communication systems. In this paper, we investigate the delay-constrained energy-efficient packet transmission. We aim to minimize the energy consumption of multiple randomly arrived packets in an additive white Gaussian noise channel subject to individual packet delay constraints, by taking into account the practical on-off circuit power consumption at the transmitter. First, we consider the offline case, by assuming that the full packet arrival information is known a priori at the transmitter, and formulate the energy minimization problem as a non-convex optimization problem. By exploiting the specific problem structure, we propose an efficient scheduling algorithm to obtain the globally optimal solution. It is shown that the optimal solution consists of two types of scheduling intervals, namely "selected-off" and "always-on" intervals, which correspond to bits-per-joule energy efficiency maximization and "lazy scheduling" rate allocation, respectively. Next, we consider the practical online case where only causal packet arrival information is available. Inspired by the optimal offline solution, we propose a new online scheme. It is shown by simulations that the proposed online scheme has a comparable performance with the optimal offline one and outperforms the design without considering on-off circuit power as well as the other heuristically designed online schemes.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1251-1260
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• 2013

This paper presents the optimal scheduling of hourly consumption in a residential community (community, neighborhood, etc.) based on real-time electricity price. The residential community encompasses individual residential loads, communal (shared) loads, and local generation. Community-aggregated loads, which include residential and communal loads, are modeled as fixed, adjustable, shiftable, and storage loads. The objective of the optimal load scheduling problem is to minimize the community-aggregated electricity payment considering the convenience of individual residents and hourly community load characteristics. Limitations are included on the hourly utility load (defined as community-aggregated load minus the local generation) that is imported from the utility grid. Lagrangian relaxation (LR) is applied to decouple the utility constraint and provide tractable subproblems. The decomposed subproblems are formulated as mixed-integer programming (MIP) problems. The proposed model would be used by community master controllers to optimize the utility load schedule and minimize the community-aggregated electricity payment. Illustrative optimal load scheduling examples of a single resident as well as an aggregated community including 200 residents are presented to show the efficiency of the proposed method based on real-time electricity price.