{"id":22525,"date":"2026-07-16T13:30:31","date_gmt":"2026-07-16T11:30:31","guid":{"rendered":"https:\/\/www.neuraldesigner.com\/use-cases\/gas-emission-reduction\/"},"modified":"2026-08-26T13:48:23","modified_gmt":"2026-08-26T11:48:23","slug":"gas-emission-reduction","status":"publish","type":"page","link":"https:\/\/www.neuraldesigner.com\/use-cases\/gas-emission-reduction\/","title":{"rendered":"Gas emissions reduction using machine learning"},"content":{"rendered":"<style>\n.nd-math-block {\n  display: block;\n  max-width: 100%;\n  overflow-x: auto;\n  margin: 1rem 0;\n  padding: 0.45rem 0;\n  text-align: center;\n}\n.nd-math-block math {\n  font-size: 1.04em;\n}\n<\/style>\n<style>.ndb{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 14px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndb *{box-sizing:border-box}.ndb a{text-decoration:none}.ndb-wrap{width:min(100%,960px);margin:0 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6px!important;color:#2d799f!important;box-shadow:none!important;border-radius:0!important}.ndb-card p:has(>img),.ndb-card figure,.ndb-lead p:has(>img),.ndb-lead figure{max-width:560px!important;margin-left:auto!important;margin-right:auto!important;text-align:center!important}.ndb-card p:has(>.nd-usecase-icon-source),.ndb-card .nd-usecase-icon-source{max-width:none!important}.ndb-card table:has(td h3){background:transparent!important;box-shadow:none!important;border:0!important;width:100%!important;table-layout:fixed!important;margin:14px 0!important}.ndb-card table:has(td h3) td{background:transparent!important;border:0!important;text-align:center!important;vertical-align:top!important;padding:16px 14px!important}.ndb-card table:has(td h3) td p{margin:0 0 12px!important;text-align:center!important;max-width:none!important}.ndb-card table:has(td h3) td p img{display:inline-block!important;width:52px!important;height:52px!important;max-width:52px!important;object-fit:contain!important;margin:0 auto!important;box-shadow:none!important;border-radius:0!important}.ndb-card table:has(td h3) td h3{color:#001233!important;font-size:16px!important;font-weight:600!important;margin:0!important;text-align:center!important;letter-spacing:.03em}body.page-child.parent-pageid-22498 main.site-main .ndb-wrap .ndb-card table td p img{width:52px!important;height:52px!important;max-width:52px!important;min-width:0!important;object-fit:contain!important;margin:0 auto!important;display:inline-block!important}main#content .ndb-card .nd-usecase-title-icon,main#content .ndb-card h3.nd-usecase-icon-title img{display:none!important}main#content .ndb-card h3.nd-usecase-icon-title{display:block!important}.ndb-card .ndb-subhead{color:#001233!important;font-size:19px!important;font-weight:600!important;margin:26px 0 8px!important;text-align:center!important}.ndb-card table:has(td h3) td p:not(:has(img)){font-size:14px!important;line-height:1.5!important;color:#51606f!important;margin:8px auto 0!important;text-align:center!important;max-width:none!important}<\/style><div class=\"ndb\"><div class=\"ndb-wrap\"><div class=\"ndb-lead\"><p>Reducing gas emissions is one of the key issues industrial companies must address nowadays, both due to ethical necessity and new legal frameworks regulating pollution levels.<\/p>\n<p>It is often difficult to determine how to reduce these emissions, usually relying on trial and error.<\/p>\n<p>A machine learning model can help identify the actions to take to reduce the atmospheric pollution generated by an installation.<\/p>\n<p>This approach promises cost savings because it enables factories to anticipate the impact of changes in their control variables on gas emissions beforehand.<\/p>\n<\/div><ul class=\"ndb-toc\"><li><a href=\"#objectives\">Objectives<\/a><\/li><li><a href=\"#benefits\">Benefits<\/a><\/li><li><a href=\"#approach\">Approach<\/a><\/li><li><a href=\"#data-set\">Data set<\/a><\/li><li><a href=\"#mathematical-model\">Mathematical model<\/a><\/li><li><a href=\"#response-optimization\">Response optimization<\/a><\/li><li><a href=\"#conclusions\">Conclusions<\/a><\/li><\/ul><div class=\"ndb-card\" id=\"objectives\"><h2>Objectives<\/h2><p>Gas emissions reduction attempts to model atmospheric pollution, allowing for an understanding of its dependency on control variables.<\/p>\n<p><img decoding=\"async\" style=\"width: 483px; max-width: 100%;\" src=\"https:\/\/www.neuraldesigner.com\/images\/gas-emissions-figure.svg\" height=\"253\" \/><\/p>\n<p>The challenge is to get information on variables that can be easily controlled without decreasing the factories&#8217; profit.<\/p>\n<\/div><div class=\"ndb-card\" id=\"benefits\"><h2>Benefits<\/h2><p>It enables making optimal adjustments to the factories&#8217; control variables to minimize gas emissions.<\/p>\n<table>\n<tbody>\n<tr>\n<td style=\"text-align: center; border: none;\">\n<p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/trending_down.svg\" \/><\/p>\n<h3>REDUCE DOWNTIME<\/h3>\n<\/td>\n<td style=\"text-align: center; border: none;\">\n<p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/search.svg\" \/><\/p>\n<h3>LOWER COSTS<\/h3>\n<\/td>\n<td style=\"text-align: center; border: none;\">\n<p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/pollution.svg\" \/><\/p>\n<h3>REDUCE GAS EMISSIONS<\/h3>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><div class=\"ndb-card\" id=\"approach\"><h2>Approach<\/h2><p>The way to reduce gas emissions is to create an approximation model that considers input variables related to our target and estimates the latter.<\/p>\n<p>Neural networks can model the correct values of the target variable to know its dependency.<\/p>\n<p>That saves costs and time on decision-making and reduces a factory&#8217;s emissions.<\/p>\n<\/div><div class=\"ndb-card\" id=\"data-set\"><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>\n<p>It comprises state, control, and performance\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Variables\">variables<\/a>.<\/p>\n<p><img decoding=\"async\" style=\"width: 482px; max-width: 100%;\" src=\"https:\/\/www.neuraldesigner.com\/images\/data-set-figure.svg\" height=\"224\" \/><\/p>\n<p><b>State variables<\/b> are those\u00a0<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>\n<p>Some examples of state variables are:<\/p>\n<ul>\n<li>The ambient temperature in a combined cycle power plant.<\/li>\n<li>The humidity in a turbine room.<\/li>\n<\/ul>\n<p><b>\u00a0<\/b><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 adjusted by the company&#8217;s technicians.<\/p>\n<p>Two examples of control variables are:<\/p>\n<ul>\n<li>The power production in a power plant.<\/li>\n<li>The combustion airflow in a furnace.<\/li>\n<\/ul>\n<p><b>\u00a0<\/b><b>Performance variables<\/b> are the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#TargetVariables\">outputs<\/a>\u00a0of the system and depend on the state and the control variables.<\/p>\n<\/div><div class=\"ndb-card\" id=\"mathematical-model\"><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>\n<p>More specifically, it relates the performance variables to the state and control variables.<\/p>\n<p><span class=\"nd-math-block\"><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><semantics><mrow><mtext>performance_variables<\/mtext><mo>=<\/mo><mtext>function<\/mtext><mo stretchy=\"false\">(<\/mo><mtext>state_variables<\/mtext><mo>,<\/mo><mtext>control_variables<\/mtext><mo stretchy=\"false\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">performance_variables = function(state_variables, control_variables)<\/annotation><\/semantics><\/math><\/span><\/p>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/\">Neural networks<\/a>\u00a0are algorithms used to fit multi-dimensional and non-linear functions from data sets.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/performance-optimization-neural-network.webp\" \/><\/p>\n<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>\n<\/div><div class=\"ndb-card\" id=\"response-optimization\"><h2>Response optimization<\/h2><p>The objective of the response optimization algorithm is to utilize the mathematical model to identify optimal operating conditions.<\/p>\n<p>Indeed, the predictive model enables us to simulate various operating scenarios and adjust the control variables to enhance efficiency.<\/p>\n<p>More specifically, performance optimization can be formulated as follows:<\/p>\n<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.<\/b><\/p>\n<p>The following figure illustrates the response optimization process.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/response-optimization.svg\" \/><\/p>\n<p>As we can see, for a given state value, <b>s<\/b>, the control value, <b>c*<\/b>, minimizes the gas emissions.<\/p>\n<\/div><div class=\"ndb-card ndb-card--accent\" id=\"conclusions\"><h2>Conclusions<\/h2><p>Reducing gas emissions allows companies to save money and time.<\/p>\n<p>Since measurement taking becomes a software issue, it can improve planning and decision-making.<\/p>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a> utilizes machine learning to construct predictive models that encompass a wide range of variables related to gas emissions reduction.<\/p>\n<\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>Reducing gas emissions is one of the key issues industrial companies must address nowadays, both due to ethical necessity and new legal frameworks regulating pollution levels. 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