An Autonomous Artificial Designer of Thermal Energy Systems: Part 1—Theoretical Considerations

1989 ◽  
Vol 111 (4) ◽  
pp. 728-733 ◽  
Author(s):  
A. S. Kott ◽  
J. H. May ◽  
C. C. Hwang
Keyword(s):  
Temperature Interval ◽  
Thermal Energy ◽  
Search Algorithm ◽  
Energy Systems ◽  
Search Space ◽  
Strong Interactions ◽  
Second Law ◽  
Chemical Transformations ◽  
Original Design ◽  
State Evaluation

A knowledge-based approach to automated conceptual design (flowsheet synthesis) of thermal energy systems with strong interactions between heat/power/chemical transformations is presented. The approach is based on a computer-oriented state-space search guided by specially developed heuristics, and makes use of Second Law (exergetic) analysis, rather than mimicking the strategy of a human designer. The original design problem, formulated in terms of an equipment units network, is decomposed and reduced to a level of a network of elementary processes, with a resulting reduction in the search space. A special form of fundamental equations for steady-state open thermodynamic systems, based on a “temperature interval” approach, allows one to determine the effects of work, heat and chemical interactions within the system on the magnitude of Second-Law infeasibility, and on the overall exergy loss over any particular temperature interval, prior to the completion of the design. Based on this treatment, a set of generalized transforming operators, a plausible move generator, and a state evaluation function are formulated. The search algorithm is discussed in detail.

An Autonomous Artificial Designer of Thermal Energy Systems: Part 2—Solution Algorithm

1989 ◽  
Vol 111 (4) ◽  
pp. 734-739 ◽  
Author(s):  
A. S. Kott ◽  
J. H. May ◽  
C. C. Hwang
Keyword(s):  
Thermal Energy ◽  
Fundamental Equation ◽  
Energy Systems ◽  
Thermal Design ◽  
Strong Interactions ◽  
Chemical Transformations ◽  
Elementary Processes ◽  
State Evaluation ◽  
Feasibility Constraints ◽  
Move Operator

A knowledge-based approach to automated conceptual design (flowsheet synthesis) of thermal energy systems with strong interactions between heat/power/chemical transformations is presented. In Part 1, formulation of a thermal design problem is stated in terms of input/output specification, component interaction, feasibility constraints, and penalty function. The problem is then decomposed in inner problems that deal with a set of elementary processes, and outer problems that find a network of components approximating the optimum set of elementary processes. A design state is evaluated using a special form of fundamental equation for steady-state open thermodynamic systems based on a “temperature interval” concept. In Part 2 of this paper, an algorithm is presented. The algorithm makes use of the state evaluation function, transformation operators, and the plausible move operator to search through a space of the design states. A simple closed-cycle gas turbine is employed to illustrate the behavior of the “artificial designer” as it advances from a certain given design to more sophisticated schemes.

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals Self-Sufficiency and Energy Savings of Renewable Thermal Energy Systems for an Energy-Sharing Community

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4284
Author(s):  
Min-Hwi Kim ◽  
Youngsub An ◽  
Hong-Jin Joo ◽  
Dong-Won Lee ◽  
Jae-Ho Yun
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Energy Systems ◽  
Hot Water ◽  
Pump System ◽  
Ground Source Heat Pumps ◽  
Self Sufficiency ◽  
Energy Sharing

Due to increased grid problems caused by renewable energy systems being used to realize zero energy buildings and communities, the importance of energy sharing and self-sufficiency of renewable energy also increased. In this study, the energy performance of an energy-sharing community was investigated to improve its energy efficiency and renewable energy self-sufficiency. For a case study, a smart village was selected via detailed simulation. In this study, the thermal energy for cooling, heating, and domestic hot water was produced by ground source heat pumps, which were integrated with thermal energy storage (TES) with solar energy systems. We observed that the ST system integrated with TES showed higher self-sufficiency with grid interaction than the PV and PVT systems. This was due to the heat pump system being connected to thermal energy storage, which was operated as an energy storage system. Consequently, we also found that the ST system had a lower operating energy, CO2 emissions, and operating costs compared with the PV and PVT systems.

How significant are the known collision and element distinctness quantum algorithms?

2004 ◽  
Vol 4 (3) ◽  
pp. 201-206
Author(s):  
L. Grover ◽  
T. Rudolph
Keyword(s):  
Search Algorithm ◽  
Quantum Algorithms ◽  
Search Space ◽  
Space Complexity ◽  
Quantum Search ◽  
Quantum Search Algorithm ◽  
Time Space ◽  
Simple Quantum ◽  
Structured Problems ◽  
Better Than

Quantum search is a technique for searching $N$ possibilities for a desired target in $O(\sqrt{N})$ steps. It has been applied in the design of quantum algorithms for several structured problems. Many of these algorithms require significant amount of quantum hardware. In this paper we propose the criterion that an algorithm which requires $O(S)$ hardware should be considered significant if it produces a speedup of better than $O\left(\sqrt{S}\right)$ over a simple quantum search algorithm. This is because a speedup of $O\left(\sqrt{S}\right)$ can be trivially obtained by dividing the search space into $S$ separate parts and handing the problem to $S$ independent processors that do a quantum search (in this paper we drop all logarithmic factors when discussing time/space complexity). Known algorithms for collision and element distinctness exactly saturate the criterion.

Design and Optimization of Integrated Distributed Energy Systems for Off-grid Buildings

2021 ◽  
pp. 1-27
Author(s):  
Jian Zhang ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Energy Storage ◽  
Optimal Design ◽  
Power Systems ◽  
Thermal Energy ◽  
Waste Heat ◽  
Carbon Dioxide Emission ◽  
Energy Systems ◽  
Solar Thermal ◽  
Solar Thermal Energy ◽  
Distributed Energy

Abstract Off-grid concepts for homes and buildings have been a fast-growing trend worldwide in the last few years because of the rapidly dropping cost of renewable energy systems and their self-sufficient nature. Off-grid homes/buildings can be enabled with various energy generation and storage technologies, however, design optimization and integration issues have not been explored sufficiently. This paper applies a multi-objective genetic algorithm (MOGA) optimization to obtain an optimal design of integrated distributed energy systems for off-grid homes in various climate regions. Distributed energy systems consisting of renewable and non-renewable power generation technologies with energy storage are employed to enable off-grid homes/buildings and meet required building electricity demands. In this study, the building types under investigation are residential homes. Multiple distributed energy resources are considered such as combined heat and power systems (CHP), solar photovoltaic (PV), solar thermal collector (STC), wind turbine (WT), as well as battery energy storage (BES) and thermal energy storage (TES). Among those technologies, CHP, PV, and WT are used to generate electricity, which satisfies the building's electric load, including electricity consumed for space heating and cooling. Solar thermal energy and waste heat recovered from CHP are used to partly supply the building's thermal load. Excess electricity and thermal energy can be stored in the BES and TES for later use. The MOGA is applied to determine the best combination of DERs and each component's size to reduce the system cost and carbon dioxide emission for different locations. Results show that the proposed optimization method can be effectively and widely applied to design integrated distributed energy systems for off-grid homes resulting in an optimal design and operation based on a trade-off between economic and environmental performance.

scholarly journals Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Octavio Camarena ◽  
Erik Cuevas ◽  
Marco Pérez-Cisneros ◽  
Fernando Fausto ◽  
Adrián González ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Optimization Method ◽  
Superior Performance ◽  
Solution Quality ◽  
Original Algorithm ◽  
Motion Models ◽  
Search Mechanism ◽  
Solitary Phase

The Locust Search (LS) algorithm is a swarm-based optimization method inspired in the natural behavior of the desert locust. LS considers the inclusion of two distinctive nature-inspired search mechanism, namely, their solitary phase and social phase operators. These interesting search schemes allow LS to overcome some of the difficulties that commonly affect other similar methods, such as premature convergence and the lack of diversity on solutions. Recently, computer vision experiments in insect tracking methods have conducted to the development of more accurate locust motion models than those produced by simple behavior observations. The most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modification to the original LS algorithm, referred to as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modified incorporating the main characteristics of the new biological formulations. As a result, LS-II improves its original capacities of exploration and exploitation of the search space. In order to test its performance, the proposed LS-II method is compared against several the state-of-the-art evolutionary methods considering a set of benchmark functions and engineering problems. Experimental results demonstrate the superior performance of the proposed approach in terms of solution quality and robustness.

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