{"id":3580,"date":"2025-11-26T11:13:19","date_gmt":"2025-11-26T10:13:19","guid":{"rendered":"https:\/\/neuraldesigner.com\/solution\/performance-optimization\/"},"modified":"2026-02-09T11:44:54","modified_gmt":"2026-02-09T10:44:54","slug":"performance-optimization","status":"publish","type":"solution","link":"https:\/\/www.neuraldesigner.com\/solutions\/performance-optimization\/","title":{"rendered":"Performance optimization using machine learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3580\" class=\"elementor elementor-3580\" data-elementor-post-type=\"solution\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5bbebbea elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5bbebbea\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7ba4680\" data-id=\"7ba4680\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6c6182a0 elementor-widget elementor-widget-text-editor\" data-id=\"6c6182a0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<section><p>Tracking the behavior of a system allows us to predict its behavior and improve its performance.<\/p><p>However, the performance of a process may depend on a large number of variables. Therefore, sophisticated methods and tools are needed to untangle all these factors effectively.<\/p><p>This article explains how to use machine learning and <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a>\u00a0to model engineering systems and optimize industrial processes.<\/p><h3>Contents<\/h3><ol><li><a href=\"#DataSet\">Data set<\/a>.<\/li><li><a href=\"#MathematicalModel\">Mathematical model<\/a>.<\/li><li><a href=\"#ResponseOptimization\">Response optimization<\/a>.<\/li><li><a href=\"#Conclusions\">Conclusions<\/a>.<\/li><\/ol><\/section><section id=\"DataSet\"><h2>Data set<\/h2><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\">data set<\/a> contains measurements from our system or process.<\/p><p>It comprises state, control, and performance <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Variables\">variables<\/a>.<\/p><p><b>State variables<\/b> are those <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputVariables\">inputs<\/a>\u00a0that determine the system&#8217;s performance and are not actionable by the company&#8217;s technicians.<\/p><p>Some examples of state variables are:<\/p><ul><li>The ambient temperature in a combined cycle power plant.<\/li><li>The water salinity in a desalination plant.<\/li><\/ul><p><b>\u00a0<\/b><\/p><p><b>Control variables<\/b> are those\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputVariables\">inputs<\/a> that determine the system&#8217;s performance and can be set by the company&#8217;s technicians.<\/p><p>Two examples of the control variables are:<\/p><ul><li>The velocity of an aircraft.<\/li><li>The combustion airflow in a furnace.<\/li><\/ul><p><b>\u00a0<\/b><\/p><p><b>Performance variables<\/b> are the system&#8217;s <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TargetVariables\">outputs<\/a>\u00a0and depend on the state and the control variables.<\/p><p>Performance optimization can consider different targets:<\/p><div style=\"text-align: center;\"><div style=\"display: inline-block; padding: 1%;\"><p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/efficiency.svg\" \/><\/p><h3>PROCESS EFFICIENCY<\/h3><\/div><div style=\"display: inline-block; padding: 1%;\"><p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/consumption.svg\" \/><\/p><h3>ENERGY CONSUMPTION<\/h3><\/div><div style=\"display: inline-block; padding: 1%;\"><p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/pollution.svg\" \/><\/p><h3>GAS EMISSIONS<\/h3><\/div><div style=\"display: inline-block; padding: 1%;\"><p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/noise.svg\" \/><\/p><h3>NOISE LEVELS<\/h3><\/div><\/div><\/section><section id=\"MathematicalModel\"><h2>Mathematical model<\/h2><p>The model of a process is a mathematical description that adequately predicts the physical system&#8217;s response to all anticipated inputs.<\/p><p>More specifically, it relates the performance variables with the state and control variables.<\/p><p>$$<br \/>performance\\_variables = function(state\\_variables, control\\_variables)<br \/>$$<\/p><p><a href=\"https:\/\/www.neuraldesigner.com\/learning\/neural-networks-tutorial\">Neural networks<\/a> are algorithms that fit multi-dimensional and non-linear functions from data sets.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/performance-optimization-neural-network.webp\" \/><\/p><p>The inputs to the neural network include the state and control variables. The outputs from the neural network are the predicted performance variables of the system for that scenario.<\/p><\/section><section id=\"ResponseOptimization\"><h2>Response optimization<\/h2><p>The objective of the response optimization algorithm is to exploit the mathematical model to look for optimal operating conditions.<\/p><p>Indeed, the predictive model allows us to simulate different operating scenarios and adjust the control variables to improve efficiency.<\/p><p>More specifically, we can formulate a performance optimization task as follows:<\/p><p style=\"border: 1px solid; text-align: center;\"><b>For a given set of states, determine the controls that minimize or maximize the performance variables.<br \/><\/b><\/p><p>The following figure illustrates the response optimization process.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/response-optimization.svg\" \/><\/p><p>As we can see, for a given state value, <b>s<\/b>, the control value, <b>c*<\/b>, minimizes the performance value.<\/p><p>A performance optimization problem might also include a set of constraints on the system&#8217;s inputs and outputs.<\/p><p>An example is to minimize the fuel consumption of an aircraft while maintaining the speed at the desired value.<\/p><\/section><p><!-- Conclusions --><\/p><section id=\"Conclusions\"><h2>Conclusions<\/h2><p>Machine learning is a powerful technique to predict the performance of engineering systems.<\/p><p>That allows us to simulate different operating scenarios and adjust the control parameters to improve efficiency.<\/p><p>Some examples of performance optimization are improving process efficiency or reducing energy consumption.<\/p><p><a href=\"https:\/\/www.neuraldesigner.com\/free-trial\">Neural Designer<\/a> uses neural networks to model the behavior of systems. It also contains response optimization algorithms to fine-tune the control variables and optimize performance.<\/p><\/section><section><h2>Related posts<\/h2><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":2229,"template":"","categories":[35],"tags":[43],"class_list":["post-3580","solution","type-solution","status-publish","has-post-thumbnail","hentry","category-industry","tag-industry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Performance optimization using machine learning<\/title>\n<meta name=\"description\" content=\"Discover how to use machine learning and response optimization to model industrial processes and optimize their performance.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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