Method and system for predicting pipeline corrosion

A machine-learning system configured to predict pipeline corrosion comprising:

• one or more receiving sections configured to acquire, input data from one or more pipelines;
• one or more data storing sections configured to store the input data;
• one or more computer processor configured to perform a prediction of pipeline corrosion comprising steps of:
• i) generating a predictive model (100) based on a neural network comprising:
• obtaining the input data stored in the data storing sections;
• providing four modules relevant to the pipeline corrosion including a water condensation rate module (106), a flow regime module (108), a corrosion rate module (110) and an operating data module (112);
• dividing the input data and feeding the divided input data into said four modules;)
• concatenating said four modules to output a depth of metal loss rate (122);
• ii) applying a supervised machine learning technique for training the predictive model (100) generated from step i);
• and
• iii) applying the predictive model (100) from step ii) to other set of the input data in order to predict the depth of metal loss rate (122);
• wherein, step ii), applying the supervised machine learning technique includes applying a first neural network (600) to the water condensation rate module (106), applying a second neural network (700) to the flow regime module (108), and applying a third neural network (800) to the corrosion rate module (110), in order to obtain initial weights of each module,
• wherein step i) the four modules are concatenated to a concatenate layer (114) to output the depth of metal loss rate (122), and
• wherein the concatenate layer (114) is processed through hidden layers (116), a long short-term memory layer (118), and the hidden layers (120) to output the depth of metal loss rate (122).

A non-transitory computer readable medium containing instruction configured for execution by one or more processors in order to cause the processors to:

• i) generating a predictive model (100) based on a neural network comprising:
• obtaining input data stored in data storing sections;
• providing four modules relevant to pipeline corrosion including a water condensation rate module (106), a flow regime module (108), a corrosion rate module (110) and an operating data module (112);
• dividing the input data and feeding the divided input data into said four modules;
• concatenating said four modules to output a depth of metal loss rate (122);
• ii) applying a supervised machine learning technique for training the predictive model (100) generated from step i);
• and
• iii) applying the predictive model (100) from step ii) to other set of the input data in order to predict the depth of metal loss rate (122);
• wherein, step ii), applying the supervised machine learning technique includes applying a first neural network (600) to the water condensation rate module (106), applying a second neural network (700) to the flow regime module (108), and applying a third neural network (800) to the corrosion rate module (110), in order to obtain initial weights of each module,
• wherein step i) the four modules are concatenated to a concatenate layer (114) to output the depth of metal loss rate (122), and
• wherein the concatenate layer (114) is processed through hidden layers (116), a long short-term memory layer (118), and the hidden layers (120) to output the depth of metal loss rate (122).

A computer program comprising instructions for implementing a method for predicting pipeline corrosion comprising steps of:

• i) generating a predictive model (100) based on a neural network comprising:
• obtaining a set of input data;
• providing four modules relevant to pipeline corrosion including a water condensation rate module (106), a flow regime module (108), a corrosion rate module (110) and an operating data module (112);
• dividing the input data and feeding the divided input data into said four modules;
• concatenating said four modules to output a depth of metal loss rate (122);
• ii) applying a supervised machine learning technique for training the predictive model (100) generated from step i);
• and
• iii) applying the predictive model (100) from step ii) to other set of the input data in order to predict the depth of metal loss rate (122);
• wherein, step ii), applying the supervised machine learning technique includes applying a first neural network (600) to the water condensation rate module (106), applying a second neural network (700) to the flow regime module (108), and applying a third neural network (800) to the corrosion rate module (110), in order to obtain initial weights of each module
• wherein step i) the four modules are concatenated to a concatenate layer (114) to output the depth of metal loss rate (122), and
• wherein the concatenate layer (114) is processed through hidden layers (116), a long short-term memory layer (118), and the hidden layers (120) to output the depth of metal loss rate (122).