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Assessment of the classification abilities of the CNS multi-parametric optimization approach by the method of logistic regression

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016年第8期27卷 629-635页

作者： Raevsky, O. A. Polianczyk, D. E. Mukhametov, A. Grigorev, V. Y. Russian Acad Sci Inst Physiologically Act Cpds Dept Comp Aided Mol Design Chernogolovka Russia

Assessment of CNS drugs/CNS candidates classification abilities of the multi-parametric optimization (CNS MPO) approach was performed by logistic regression. It was found that the five out of the six separately used physical-chemical properties (topological polar surface area, number of hydrogen-bonded donor atoms, basicity, lipophilicity of compound in neutral form and at pH = 7.4) provided accuracy of recognition below 60%. Only the descriptor of molecular weight (MW) could correctly classify two-thirds of the studied compounds. Aggregation of all six properties in the MPOscore did not improve the classification, which was worse than the classification using only MW. The results of our study demonstrate the imperfection of the CNS MPO approach;in its current form it is not very useful for computer design of new, effective CNS drugs.

关键词： CNS MPO multi-parametric optimization logistic regression blood-brain barrier

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An approximation algorithm for a general class of multi-parametric optimization problems

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JOURNAL OF COMBINATORIAL optimization 2022年第3期44卷 1459-1494页

作者： Helfrich, Stephan Herzel, Arne Ruzika, Stefan Thielen, Clemens Univ Kaiserslautern Dept Math Paul Ehrlich Str 14 D-67663 Kaiserslautern Germany Weihenstephan Triesdorf Univ Appl Sci TUM Campus Straubing Biotechnol & Sustainabil Essigberg 3 D-94315 Straubing Germany Tech Univ Munich Dept Math Boltzmannstr 3 D-85748 Garching Germany

In a widely-studied class of multi-parametric optimization problems, the objective value of each solution is an affine function of real-valued parameters. Then, the goal is to provide an optimal solution set, i.e., a set containing an optimal solution for each nonparametric problem obtained by fixing a parameter vector. For many multi-parametric optimization problems, however, an optimal solution set of minimum cardinality can contain super-polynomially many solutions. Consequently, no polynomial-time exact algorithms can exist for these problems even if P = NP. We propose an approximation method that is applicable to a general class of multi-parametric optimization problems and outputs a set of solutions with cardinality polynomial in the instance size and the inverse of the approximation guarantee. This method lifts approximation algorithms for non-parametric optimization problems to their parametric version and provides an approximation guarantee that is arbitrarily close to the approximation guarantee of the approximation algorithm for the non-parametric problem. If the non-parametric problem can be solved exactly in polynomial time or if an FPTAS is available, our algorithm is an FPTAS. Further, we show that, for any given approximation guarantee, the minimum cardinality of an approximation set is, in general, not l-approximable for any natural number l less or equal to the number of parameters, and we discuss applications of our results to classical multi-parametric combinatorial optimizations problems. In particular, we obtain an FPTAS for the multi-parametricminimum s-t-cut problem, an FPTAS for the multi-parametric knapsack problem, as well as an approximation algorithm for the multi-parametric maximization of independence systems problem.

关键词： multi-parametric optimization Approximation algorithm multi-parametric minimum s-t-cut problem multi-parametric knapsack problem multi-parametric maximization of independence systems

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Form tolerance analysis and multi-parametric optimization of meso deep square hole EDMed on Inconel-718 plate using GRA method

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GREY SYSTEMS-THEORY AND APPLICATION 2021年第4期11卷 664-680页

作者： Kumar, Sandeep Dhanabalan, S. Anna Univ Chennai Mech Engn Chennai Tamil Nadu India M Kumarasamy Coll Engn Mech Engn Karur India

Purpose - The main objective of this experimental work is to analyze and measure the form tolerances namely flatness and squareness while machining a meso deep hole in EDM on Inconel-718 material plate. Design/methodology/approach - The experiments were performed on 15 amps rated SPARKONIX-EDM as per DOE (design of experiments). Kerosene was used as a dielectric along with constant pressure of 0.2 kg/cm(2) for all trial runs. The currents T-on and T-off were selected as process constraints to conduct experimental trials. The MRR, EWR, machining time and form tolerances were considered as output responses. The experimental outcomes were optimized by hybrid optimization using Taguchi and GRA (grey relational analysis) method. Findings - The EDM process parameters for Ni-based super alloy namely Inconel-718 had optimized by using GRA method coupled with Taguchi method. The optimum solution has been calculated for MRR, EWR, machining time and form tolerances namely squareness and flatness. The optimized parameters for the output responses in EDM process are Peak current (Ip) 12 Amps, 400 mu s T-on (pulse on time) and 10 ms T-off (pulse off time). An attempt had also been made to attain Max. and Min. Evaluation of MRR and form tolerances, respectively. The attained optimum outcomes had also been examined through a real experiment and established to be satisfactory. Originality/value - The optimized parameters by multi-parametric optimization showed the considerable improvement in the process and will facilitate the defense, aerospace and EDM industries to improve their productivity with closer tolerances.

关键词： Electrical discharge machining (EDM) Square-shape brass electrode Material removal rate (MRR) Coordinate measurement machine (CMM) Grey relational analysis (GRA) multi-parametric optimization

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A High-level Synthesis Design Flow from ESL to RTL with multi-parametric optimization Objective

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IETE JOURNAL OF RESEARCH 2011年第2期57卷 172-189页

作者： Sengupta, Anirban Sedaghat, Reza Ryerson Univ Dept Elect & Comp Engn Toronto ON Canada

High-level synthesis (HLS) has emerged as the most sophisticated way to bridge the gap between electronic system level (ESL) and its respective structural building block at the register transfer level (RTL). As the growth of system complexity rapidly increases, the gap between high level and RTL needs to be filled. Much advancement has been made in the area of HLS, but none of the works have focused on a formal design methodology that bridges the gap from ESL to RTL considering multi-parametric optimization requirements. This paper exclusively focuses on the formal steps required for multi-parametric optimized HLS design flow. This is significant for industrial projects as well as for the development of fully automated HLS tools for the current generation of portable devices and high-end applications. The design flow initiates with the mathematical model of the application, performs multi-objective design space exploration and finally shows all the steps necessary after exploration for the HLS design. This paper explicitly focuses on highlighting the design flow with optimization of three parameters area, execution time and power consumption during HLS design while working under the prerequisite of stringent operational constraints. The implementation of the proposed method on Field-programmable Gate Array and the chip layout generation will also be presented.

关键词： High-level synthesis design flow multi-parametric optimization Portable devices Stringent operational constraints

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Predictive modeling and multi-parametric optimization of catalytic pyrolysis of disposable face mask using a combinational approach of response surface methodology and machine learning: Insights into the influence of waste-derived catalyst

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JOURNAL OF CLEANER PRODUCTION 2024年 466卷

作者： Hooda, Sanjeevani Patel, Pushpraj Mondal, Prasenjit Indian Inst Technol Roorkee Dept Chem Engn Roorkee 247667 Uttarakhand India

The current study aims to model and optimize the catalytic pyrolysis of face masks incorporating a waste-derived catalyst to analyze the effect of process parameters (temperature, feed-to-catalyst ratio, and inert gas flow rate) on the oil yield of the process. An integrated approach of response surface methodology (R2-0.95) and machine learning (decision trees regression, R2-0.83) demonstrated a higher prediction accuracy and lower error margins. Explainable artificial intelligence tools spotlighted temperature to be the predominant parameter followed by feed-to-catalyst ratio. Experimental oil yield (13.5%) obtained at optimized parameters (516 degrees C temperature, 3:1 feed-to-catalyst ratio, and 163 mL/min inert gas flow rate) was compared with those predicted through response surface methodology (13.7%) and decision trees regression (13.12%), showcasing an absolute error range of 0.2-0.4 wt%. Gas chromatography-mass spectroscopy analysis of oil highlighted the presence of silica compounds that can be extracted as value-added chemicals. Further, the overall percentage of naphthene's, paraffins, and olefins in the oil were approximated to be around 40.5% based on the peak area. The presence of hydrocarbons in oil having carbon numbers predominantly in the range of gasoline and diesel as well as a high heating value of 36.56 MJ/kg demonstrated its potential to be used as a fuel. The present study demonstrates the feasibility of valorizing the face mask into energy-dense oil comparable to commercial range fuel using a spent adsorbent based catalyst.

关键词： multi-parametric optimization Waste-to-energy Circular economy Waste-derived catalyst Plastic waste valorization

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multi-parametric MINLP optimization study of a composite I beam floor system

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ENGINEERING STRUCTURES 2017年 130卷 316-335页

作者： Kravanja, S. Zula, T. Klansek, U. Univ Maribor Fac Civil Engn Transportat Engn & Architecture Smetanova 17 SI-2000 Maribor Slovenia

This paper deals with a comparative study of an optimal design regarding composite steel and concrete floor structures based on the performed multi-parametric mixed-integer non-linear programming (MINLP) approach, and Eurocode specifications. The optimization models COMBOPT (COMposite Beam optimization) were developed and cost optimizations of the structure alternatives were performed. A series of MINLP optimizations were executed over a wide range of various design parameters: different material unit prices, hourly labour costs, imposed loads, structure spans, steel and concrete grades, welded and standard steel sections, plastic and elastic cross-sectional resistances, different positions of the neutral axis and different locations of the centre of gravity axis of the transformed (all steel) section. The Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm was applied. The minimal self-manufacturing costs of the structure, steel and concrete grades and standard sizes were obtained through each individual MINLP optimization. All the results were analysed and compared. Comparative diagrams and a recommended design for a composite floor system were determined. In addition, this study answered some questions regarding the influence of unit prices on the optimal design, the most suitable position of the neutral axis, the competitive spans of the structures with welded and standard steel sections, the competitiveness of the plastic cross-section resistance when compared to the elastic resistance, the spans and loads where the ultimate and the serviceability limit states are predominant and the adequate location of the centre of gravity axis of the transformed (all steel) section. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Composite structures Cost optimization Structure optimization multi-parametric optimization Mixed-integer non-linear programming MINLP

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Fast and Lightweight: A real-time parallelizable MPC for embedded systems☆

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EUROPEAN JOURNAL OF CONTROL 2025年 83卷

作者： Jiang, Yuning Fedorova, Kristina Su, Junyan Oravec, Juraj Houska, Boris Jones, Colin N. Ecole Polytech Fed Lausanne Automat Control Lab Lausanne Switzerland Slovak Univ Technol Bratislava Fac Chem & Food Technol Bratislava Slovakia City Univ Hong Kong Dept Data Sci Hong Kong Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China

This paper presents a parallelizable suboptimal Model Predictive Control (MPC) design framework for structured linear systems with polytopic state and control constraints. The proposed real-time control policy addresses structured large-scale quadratic programming (QP) problems by deriving the control action by evaluating a finite set of piece-wise affine functions (PWA). These PWA functions are precomputed offline as explicit solutions to small-scale multiparametric QP problems that tailor this method for industrial- oriented or embedded implementation. Prioritizing computational efficiency over optimality, the proposed MPC controller ensures real-time feasibility within stringent time constraints. The key contributions include the derivation of a lower bound on the fixed number of algorithm iterations required to guarantee the closed- loop performance under assumptions and an open-source C-code library, ParExMPC, based on the proposed framework. Numerical simulations highlight the scalability of the method, accommodating systems with a high number of decision variables and extended control horizons-well beyond the capabilities of existing explicit MPC methods. Furthermore, the developed implementation of the proposed close-to-optimal control method demonstrates superior runtime performance compared to state-of-the-art implicit MPC solutions, which rely on online optimization.

关键词： Model predictive control multi-parametric optimization Real-time control Embedded control Distributed optimization Open-source implementation

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Dynamic risk-based process design and operational optimization via multi-parametric programming

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DIGITAL CHEMICAL ENGINEERING 2023年 7卷

作者： Ali, Moustafa Cai, Xiaoqing Khan, Faisal I. Pistikopoulos, Efstratios N. Tian, Yuhe Texas A&M Univ Texas A&M Energy Inst College Stn TX 77843 USA Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77843 USA Texas A&M Univ Mary Kay O Connor Proc Safety Ctr Artie McFerrin Dept Chem Engn College Stn TX 77843 USA West Virginia Univ Dept Chem & Biomed Engn Morgantown WV 26506 USA

We present a dynamic risk-based process design and multi-parametric model predictive control optimization approach for real-time process safety management in chemical process systems. A dynamic risk indicator is used to monitor process safety performance considering fault probability and severity, as an explicit function of safety-critical process variables deviation from nominal operating conditions. Process design-aware risk-based multi-parametric model predictive control strategies are then derived which offer the advantages to: (i) integrate safety-critical variable bounds as path constraints, (ii) control risk based on multivariate process dynamics under disturbances, and (iii) provide model-based risk propagation trend forecast. A dynamic optimization problem is then formulated, the solution of which can yield optimal risk control actions, process design values, and/or real-time operating set points. The potential and effectiveness of the proposed approach to systematically account for interactions and trade-offs of multiple decision layers toward improving process safety and efficiency are showcased in a real-world example, the safety-critical control of a continuous stirred tank reactor at T2 Laboratories.

关键词： Dynamic risk assessment Fault prognosis Explicit model predictive control multi-parametric optimization Dynamic optimization

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Performance Analysis of Single-Phase Space Thermal Radiators and optimization Through Taguchi-Neuro-Genetic Approach

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JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS 2022年第6期14卷 061012页

作者： Chiranjeevi, P. B. Ashok, V. Srinivasan, K. Sundararajan, T. Indian Space Res Org Human Space Flight Ctr Bengaluru 560094 Karnataka India Indian Space Res Org Vikram Sarabhai Space Ctr Trivandrum 695586 Kerala India Indian Inst Technol Madras Dept Mech Engn Chennai 600036 Tamil Nadu India

In the thermal management of spacecraft, space thermal radiators play a vital role as heat sinks. A serial radiator with proven advantages in ground applications is proposed and analyzed for space applications. From the performance analysis, specific heat rejection (SHR) of serial radiator is found to be higher than parallel radiator by 80% for maximum diameter of the tube, 47% for maximum thickness of the fin, and 75% for maximum pitch of the tubes under consideration. Also, serial radiator requires four times higher pumping power than parallel radiator with geometric parameters and a maximum mass flowrate under consideration. In serial radiators, the cross conduction between the fins has a significant effect on its thermal performance. Thus, conjugate heat transfer simulations and optimization operations are to be performed iteratively to optimize the serial radiator, which is computationally costly. To reduce the computational time, artificial neural network (ANN) is trained using conjugate heat transfer simulations data and combined with the genetic algorithm (GA) to perform optimization. Taguchi's orthogonal arrays provided the partial fraction of conjugate heat transfer simulations set to train the ANN. Taguchi-Neuro-Genetic approach, a process that combines the features of three powerful techniques in different optimization phases, is used to optimize both parallel and serial radiators. The optimization aims to obtain a configuration that provides the lowest mass and lowest pumping power requirement for given heat rejection. optimization results show that the conventional parallel radiator is about 20% heavier and requires about 35% more pumping power than the proposed serial radiator.

关键词： conjugate heat transfer analysis design of experiments Taguchi's orthogonal arrays neuro-genetic optimization multi-parametric optimization extended surfaces fins heat exchangers heat pipes heat transfer enhancement

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Application of GRA method for multi-objective optimization of roller burnishing process parameters using a carbide tool on high carbon steel (AISI-1040)

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GREY SYSTEMS-THEORY AND APPLICATION 2019年第4期9卷 449-463页

作者： Kumar, Sandeep Mitra, Bedasruti Kumar, Naresh Anna Univ Dept Mech Engn Chennai Tamil Nadu India Tata Consultancy Serv Ltd Mumbai Maharashtra India CT Inst Engn Management & Technol Dept Mech Engn Jalandhar Punjab India

Purpose The purpose of this paper is to analyze and optimize the roller burnishing process parameters using the design of experiments and grey relational analysis (GRA). Design/methodology/approach In this experimental work, the carbide burnishing tool has been selected for the machining of AISI-1040 high carbon steel to get better product quality and satisfactory machining characteristics. The material surface condition while machining, burnishing tool speed, feed rate, depth of penetration and No. of passes have been selected as process constraints to conduct experimental trials. Findings The surface roughness (SR) and surface hardness were considered as output responses. The experimental outcomes optimized by multi-parametric optimization showed considerable improvement in the process. The roller speed and number of passes are the most significant parameters for surface hardness, whereas the surface condition and roller penetration depth have the most significance on SR. Research limitations/implications - The GRA method shows the 0.03376 improvement in grey relational grade between the experimental values and the predicted values. Practical implications - The experimental outcomes optimized by multi-parametric optimization showed the considerable improvement in the process and will facilitate steel industries to enhance and improve productivity while burnishing high carbon steel (AISI-1040). Originality/value This research represents valid work, and the authors have no conflict of interests.

关键词： Taguchi method multi-parametric optimization AISI-1040 steel Burnishing process Carbide tool Grey relational analysis method Surface hardness

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