Leapfrogging with improved initialization and its applications: Demonstration of leapfrogging on horizon predictive control of a heat exchanger

Leapfrogging (LF) is a recently developed optimization technique that initially places players in random spots in the feasible decision variable space [1]. The approach to reach the global optimum is by "Leaping" the player with the worst objective function (OF) value "Over" the player with the best OF value into the reflected hyper volume that connects the player with the best and the worst OF until the players converge at an optimum [1]. LF has several advantages compared to other optimization techniques in terms of computational efficiency and higher probability to reach the global optimum [1]. This is demonstrated in several applications [1-7].The main focus of this work is to develop LF [8] by exploring the initialization step through a fundamental analysis and supporting the developed technique with mathematical truths. In this improvisation the OF surface is initially explored with a large number of players, the players are sorted in ascending order of their OF values and the top few players are selected to continue with the optimization technique. This improvement is found to increase the probability of finding the global optimum as one of the initial players is placed in the vicinity of the global optimum during initialization and thus draws all the other players towards it.In order to establish the applicability of LF and its improvement on process engineering applications, this work focusses on implementation of original and modified LF to model a pilot scale Shell and Tube Heat Exchanger (HX 001) process, which is nonlinear. Steam is used to increase the temperature of the water on the cold side. For this study, the outlet temperature of the cold side fluid is considered as the control variable (CV). The hot side steam valve opening is considered as the manipulated variable (MV). This CV-MV relation is modeled using original and modified LF to find the model parameters that best fit the experimental skyline function generated for modeling purpose in the Unit Operations Lab, OSU-Stillwater. Next, the model parameters are used to implement a horizon predictive control on the HX001 process to control the CV

Accelerating Convergence of Leapfrogging Optimization - Applications to Nonlinear Process Modeling and Nonlinear Model Predictive Control

Conventionally used optimization methods in chemical engineering applications such as linear programming (LP), Levenberg-Marquardt and sequential quadratic programming (SQP) handle nonlinear objective function (OF) surfaces by linearizing or assuming quadratic behavior of the surfaces [1]. Process modeling and nonlinear model predictive control (NMPC) applications, however, present OF surfaces with surface aberrations such as steep slopes, discontinuities, and hard constraints which require a robust and efficient optimization method. Therefore, an optimization method that can handle surface aberrations is required.Leapfrogging (LF) is a recently developed direct search optimization method, potentially best-in-class, which can handle surface aberrations. LF starts with a set of players (trial solutions), randomly placed in the decision variable (DV) space. The worst player (player with the worst OF value) leaps over the best player into a reflected hypervolume [2]. The leapovers continue until all the players converge. LF is robust and efficient - with minimal computation effort (compared to conventional optimization methods), it can handle the challenges posed by nonlinear OF surfaces. LF was demonstrated on over 40 test functions and several modeling and NMPC applications. Rigorous fundamental analysis of LF is required - for a finer understanding of the method, exploring opportunities for improvement and scaling LF applications to large scale systems.This work is focused on exploring and analyzing methods to accelerate convergence of LF, demonstrating application credibility on nonlinear process modeling of steady state binary distillation and NMPC of a binary distillation column. Accelerating convergence opens the doors for using LF in large scale problems that have several hundred variables such as real time optimization and refinery planning where computational effort and time are of essence. Distillation modeling is constrained, nonlinear, and has optimum confined to a narrow region; distillation control is multivariable, interacting, nonlinear and has severe disturbances.Completion of this work will provide new fundamental understanding of LF which is critical for creating opportunities for algorithm improvement. Demonstrating application to nonlinear process modeling and NMPC will create application credibility, reveal practicality and serve as proof of concept that LF can be an optimizer of choice for use in the process control community.Chemical Engineerin

Incorporation of the Generalized Tsk Models in Model Predictive Control

The generalized TSK (GTSK) modeling approach is proved to provide accurate model prediction and to alleviate the computational burden. The scope of this study is to incorporate the GTSK models in the nonlinear model predictive control (NMPC) to improve the overall performance and reliability of NMPC. A novel global optimization method, the Leapfrogging technique, is also used to further improve the NMPC's computational efficiency. Another innovation, the "sawtooth" pattern is used as input signal to generate the GTSK model. The experiments and tests are conducted on a nonlinear process simulation system, in which the NMPC control algorithm was embedded. The virtual process in this simulator is fourth-order-plus-dead-time (FOPDT) process with a nonlinear gain and the environmental effect (noise and disturbance). The controlled process is subject to both soft and hard constraints - soft on both the controlled and the auxiliary variable, and hard on both the limits and rate of change of the manipulated variable. The NMPC performance is evaluated via several simulation experiments, which involved constraint handling, interactions and process nonlinearity. The use of a GTSK model and Leapfrogging as an optimizer were demonstrated as effective for nonlinear model predictive control. The nonlinear model is firstly developed by using GTSK approach. The prediction accuracy of the GTSK model was illustrated and quantified by a comparison with SOPDT model. The GTSK model was much better. The performance of GTSK MPC controller is evaluated via seven sets of dynamic control simulation. The controller showed desirable performance for disturbance rejection, set point tracking, constraint handling, and comprehensive environmental effect handling.School of Chemical Engineerin

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

The Design and Manufacture of a Vertical Takeoff and Landing Fixed Wing Unmanned Aerial System for Use in Atmospheric Sampling

The creation of a prototype vertical takeoff and landing fixed wing unmanned aerial system for use in sampling of trace gases, aerosols, and volatile organic compounds is described. A conceptual design framework is devised based upon desired performance characteristics and conclusions drawn from background research into platform layout, energy sources, and construction methods. Optimized designs are produced according to this framework, and the most appropriate option for the application serves as the foundation in producing a detailed design of the platform. The methods employed in manufacturing the aircraft’s individual components as well as the assembly of the system as a whole are described. The testing involved in validating critical systems is presented, culminating in an accounts of the aircraft’s flight tests. The lessons learned from this first implementation are highlighted and then applied to produce an improved conceptual design

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

MULTIOBJECTIVE OPTIMIZATION MODELS AND SOLUTION METHODS FOR PLANNING LAND DEVELOPMENT USING MINIMUM SPANNING TREES, LAGRANGIAN RELAXATION AND DECOMPOSITION TECHNIQUES

The land development problem is presented as the optimization of a weighted average of the objectives of three or more stakeholders, subject to develop within bounds residential, industrial and commercial areas that meet governmental goals. The work is broken into three main sections. First, a mixed integer formulation of the problem is presented along with an algorithm based on decomposition techniques that numerically has proven to outperform other solution methods. Second, a quadratic mixed integer programming formulation is presented including a compactness measure as applied to land development. Finally, to prevent the proliferation of sprawl a new measure of compactness that involves the use of the minimum spanning tree is embedded into a mixed integer programming formulation. Despite the exponential number of variables and constraints required to define the minimum spanning tree, this problem was solved using a hybrid algorithm developed in this research

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Modern meat: the next generation of meat from cells

Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available