scholarly journals Multi-Objective Optimization of HVAC Operation for Balancing Energy Use and Occupant Comfort in Educational Buildings

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2847
Author(s):  
Alessandro Franco ◽  
Carlo Bartoli ◽  
Paolo Conti ◽  
Lorenzo Miserocchi ◽  
Daniele Testi
Keyword(s):  
Energy Use ◽  
Hvac Systems ◽  
Climatic Conditions ◽  
Operating Conditions ◽  
Comfort Level ◽  
Multi Objective Optimization ◽  
Technical Standards ◽  
Safety Standards ◽  
Multi Objective ◽  
Occupant Comfort

The paper provides a methodology for the optimal control of heating, ventilation, and air conditioning (HVAC) systems used in public buildings, with the purpose of obtaining high comfort and safety standards along with energy efficiency. The combination of the two concurrent objectives of minimizing energy use and guaranteeing high standards of occupant comfort is obtained by means of multi-objective optimization, in which a comfort model is combined along with a dynamic energy model of the building. The use of dynamic setpoints for the HVAC and the inclusion of comfort indicators represent a step forward, compared to the current design and operation procedures suggested by technical standards. The utilization of the proposed methodology is tested with reference to a case study, represented by an academic building used by the University of Pisa for educational purposes, whose extensive and variable occupancy can help to emphasize the importance of comfort in the operation of HVAC systems in different climatic conditions and with different occupancy profiles. We show how this optimization brings interesting results in terms of energy-saving (up to 30%), obtaining an increased comfort level (of more than 25%) compared to the operating conditions suggested by technical standards.

scholarly journals Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

2020 ◽  
pp. 105-113
Author(s):  
M. Farsi
Keyword(s):  
Pareto Front ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Base Case ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  

Multi-objective optimization of an industrial slurry phase ethylene polymerization reactor

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Amit K. Thakur ◽  
Santosh K. Gupta ◽  
Rahul Kumar ◽  
Nilanjana Banerjee ◽  
Pranava Chaudhari
Keyword(s):  
Ethylene Polymerization ◽  
Operating Cost ◽  
Operating Conditions ◽  
Multi Objective Optimization ◽  
Reactor Model ◽  
Optimal Operating ◽  
Nsga Ii ◽  
Multi Objective ◽  
Maximum Productivity ◽  
Slurry Phase

Abstract Slurry polymerization processes using Zeigler–Natta catalysts are most widely used for the production of polyethylene due to their several advantages over other processes. Optimal operating conditions are required to obtain the maximum productivity of the polymer at minimal cost while ensuring operational safety in the slurry phase ethylene polymerization reactors. The main focus of this multi-objective optimization study is to obtain the optimal operating conditions corresponding to the maximization of productivity and yield at a minimal operating cost. The tuned reactor model has been optimized. The single objective optimization (SOO) and multi-objective optimization (MOO) problems are solved using non-dominating sorting genetic algorithm-II (NSGA-II). A complete range of Pareto optimal solutions are obtained to obtain the maximum productivity and polymer yield at different input costs.

Multi-Objective Optimization Model Development to Support Sizing Decisions for a Novel Reciprocating Steam Engine Technology

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
J. M. Hamel ◽  
Devin Allphin ◽  
Joshua Elroy
Keyword(s):  
Computational Model ◽  
Design Optimization ◽  
Kinematic Model ◽  
Operating Conditions ◽  
Steam Engine ◽  
System Model ◽  
System Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Crank Angle

A system-level computational model of a recently patented and prototyped novel steam engine technology was developed from first principles for the express purpose of performing design optimization studies for the engine's inventors. The developed system model consists of numerous submodels including a flow model of the intake process, a dynamic model of the intake valve response, a pressure model of the engine cylinder, a kinematic model of the engine piston, and an output model that determines engine performance parameters. A crank-angle discretization strategy was employed to capture the performance of engine throughout a full cycle of operation, thus requiring all engine design submodels to be evaluated at each crank angle of interest. To produce a system model with sufficient computational speed to be useful within optimization algorithms, which must exercise the system level model repeatedly, various simplifying assumptions and modeling approximations were utilized. The model was tested by performing a series of multi-objective design optimization case studies using the geometry and operating conditions of the prototype engine as a baseline. The results produced were determined to properly capture the fundamental behavior of the engine as observed in the operation of the prototype and demonstrated that the design of engine technology could be improved over the baseline using the developed computational model. Furthermore, the results of this study demonstrate the applicability of using a multi-objective optimization-driven approach to conduct conceptual design efforts for various engine system technologies.

scholarly journals Multi-objective optimization of an industrial styrene reactor using Harmony Search Algorithm

2013 ◽  
pp. 190-196
Author(s):  
Saurabh Shukla ◽  
Ankit Anand
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Operating Conditions ◽  
Multi Objective Optimization ◽  
Industrial Practice ◽  
Multi Objective ◽  
Decision Variables ◽  
Optimization Study ◽  
Study Results

Multi-objective optimization of industrial styrene reactor is done using Harmony Search algorithm. Harmony search algorithm is a recently developed meta-heuristic algorithm which is inspired by musical improvisation process aimed towards obtaining the best harmony. Three objective functions – productivity, selectivity and yield are optimized to get best combination of decision variables for styrene reactor. All possible cases of single and multi-objective optimization have been considered. Pareto optimal sets are obtained as a result of the optimization study. Results reveal that optimized solution using harmony search algorithm gives better operating conditions than industrial practice.

scholarly journals Optimisasi Multi-objektif pada Rekonfigurasi Jaringan Distribusi Tenaga Listrik dengan Integrasi Pembangkit Terdistribusi Menggunakan Metode Sistem Kekebalan Buatan

2020 ◽  
Vol 12 (2) ◽  
pp. 57-71
Author(s):  
Ramadoni Syahputra ◽  
Indah Soesanti
Keyword(s):  
Network Optimization ◽  
Load Balance ◽  
Distribution Network ◽  
Reducing Power ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Network Reconfiguration ◽  
Multi Objective Optimization ◽  
Power Losses ◽  
Multi Objective

This study proposes a multi-objective optimization for power distribution network reconfiguration by integrating distributed generators using an artificial immune system (AIS) method. The most effective and inexpensive technique in reducing power losses in distribution networks is optimizing the network reconfiguration. On the other hand, small to medium scale renewable energy power plant applications are growing rapidly. These power plants are operated on-grid to a distribution network, known as distributed generation (DG). The presence of DG in this distribution network poses new challenges in distribution network operations. In this study, the distribution network optimization was carried out using the AIS method. In optimization, the goal to be achieved is not only one objective but should be multiple objectives. Multi-objective optimization aims to reduce power losses, improve the voltage profile, and maintain a maintained network load balance. The AIS method has the advantage of fast convergence and avoids local minima. To test the superiority of the AIS method, the distribution network optimization with and without DG integration was carried out for the 33-bus and 71-bus models of the IEEE standard distribution networks. The results show that the AIS method can produce better system operating conditions than before the optimization. The parameters for the success of the optimization are minimal active power losses, suitable voltage profiles, and maintained load balance. This optimization has successfully increased the efficiency of the distribution network by an average of 0.61%.

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