Gas emissions reduction is one of the key issues industrial companies have to address nowadays, both because of an ethical necessity and the new legal frameworks regulating pollution levels. It is often difficult to detect how to reduce these emissions, appealing to trial and error. A machine learning model can help locate what actions to put in motion to reduce the atmospheric pollution produced by an installation.
This approach promises cost savings because it makes it possible to know how changes in factories’ control variables will affect gas emissions beforehand.
Gas emissions reduction tries to model atmospheric pollution, so it is possible to see how its dependency on the control variables works.
The challenge is to get information on variables that can be easily controlled without decreasing the factories’ profit.
It allows performing the optimum changes in the factories’ control variables to reduce gas emissions.
REDUCE GAS EMISSIONS
The way to reduce gas emissions is to create an approximation model that considers input variables related to our target and estimates the latter.
Neural networks can model the correct values of the target variable to know its dependency.
That saves costs and time on decision-making and reduces a factory’s emissions.
The data set contains measurements from our system or process.
It comprises state, control, and performance variables.
State variables are those inputs that determine the system’s performance and are not actionable by the company’s technicians.
Some examples of state variables are:
- The ambient temperature in a combined cycle power plant.
- The humidity in a turbine room.
Control variables are those inputs that determine the performance of the system and can be set by the company’s technicians.
Two examples of control variables are:
- The power production in a power plant.
- The combustion airflow in a furnace.
Performance variables are the outputs of the system and depend on the state and the control variables.
The model of a process is a mathematical description that adequately predicts the physical system’s response to all anticipated inputs.
More specifically, it relates the performance variables with the state and control variables.
performance\_variables = function(state\_variables, control\_variables)
Neural networks are algorithms used to fit multi-dimensional and non-linear functions from data sets.
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.
The objective of the response optimization algorithm is to exploit the mathematical model to look for optimal operating conditions.
Indeed, the predictive model allows us to simulate different operating scenarios and adjust the control variables to improve efficiency.
More specifically, performance optimization can be formulated as follows:
The next figure illustrates the response optimization process.
As we can see, for a given state value, s, the control value, c*, minimizes the gas emissions.
Gas emissions reduction makes companies save money and time. Since measure taking becomes a software issue, which can improve planning and decision making.
Neural Designer uses machine learning to build predictive models that represent a broad range of variables associated with gas emissions reduction.