A method and system for electrically heating a marine pipeline

Abstract

The application discloses a kind of ship pipeline electric heating method and system, belong to ship pipeline heating technical field.The application is trained to the initial neural network model by historical temperature data and the historical heating power data, obtains ship pipeline electric heating control prediction model;Afterwards, real-time temperature data and real-time heating power data are input into ship pipeline electric heating control prediction model, obtain electric heating control prediction data, and combine real-time temperature data to calculate electric heating demand power prediction value, and obtain electric heating demand power prediction value, and then based on electric heating demand power prediction value, the electric heating process of ship pipeline is adjusted, not only avoid the heating effect caused by the heating effect of the existing simple power heating is not good, but also can be aimed at complex and changeable temperature fluctuation in polar navigation, reduce electric heat tracing energy consumption, improve the accuracy and reliability of ship pipeline heating, ensure that the ship can operate normally in polar environment.

Description

Technical Field

[0001] This invention belongs to the field of marine pipeline heating technology, and particularly relates to a method and system for electric heating of marine pipelines. Background Technology

[0002] With global warming and the gradual reduction of polar ice caps, polar navigation has become an important direction for shipping development. However, the extreme low temperatures of the polar environment place higher demands on ships, especially on the protection and antifreeze of ship pipelines. As a critical channel for transporting liquids such as fuel oil, hydraulic oil, fresh water, and wastewater, the reliable operation of ship pipelines in extreme low-temperature environments directly affects the functional stability of the ship's propulsion and life support systems. If the fluids in the pipelines freeze or their flowability decreases, it may lead to serious accidents such as pipeline rupture, valve failure, and power interruption, and even threaten the ship's navigation safety and mission execution capabilities in the polar regions. Therefore, heating and insulating ship pipelines for polar navigation is crucial to ensuring safe operation in polar regions.

[0003] Currently, for heating and insulation of ship pipelines under extremely low navigation conditions, most methods involve wrapping with insulation materials, steam heating, or electric heat tracing. However, in polar environments, the external temperature varies greatly. Wrapping with insulation materials is insufficient to completely isolate external cold sources and cannot dynamically respond to temperature fluctuations. Steam heating is prone to localized overheating or icing blockage of the pipelines, resulting in unsatisfactory heating effects and inaccurate heating of the ship's pipelines. In addition, existing electric heat tracing is a fixed-power heating method, which can only achieve good heating effects within its set temperature range. Outside of this range, the heating and insulation effect of the ship's pipelines will drop significantly, and the large temperature fluctuations also lead to excessively high energy consumption for current electric heat tracing methods. Therefore, there is an urgent need for an electric heating method and system for ship pipelines to overcome the shortcomings of existing technologies. Summary of the Invention

[0004] The present invention aims to provide a method and system for electric heating of ship pipelines to solve the above-mentioned technical problems. By using a ship pipeline electric heating control prediction model to predict heating control, the electric heating process of ship pipelines can be regulated, thereby improving the accuracy of ship pipeline electric heating.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a method for electrically heating ship pipelines, comprising:

[0006] Historical temperature data, historical heating power data, and an initial neural network model are acquired, and the initial neural network model is trained based on the historical temperature data and the historical heating power data to obtain a prediction model for ship pipeline electric heating control.

[0007] Acquire real-time temperature data and real-time heating power data, and input the real-time temperature data and real-time heating power data into the ship pipeline electric heating control prediction model to obtain electric heating control prediction data;

[0008] The predicted value of electric heating power demand is calculated based on the electric heating control prediction data and the real-time temperature data, and the electric heating process of the ship's pipeline is adjusted according to the predicted value of electric heating power demand.

[0009] It is understood that, compared with the prior art, the present invention trains the initial neural network model with historical temperature data and historical heating power data to obtain a ship pipeline electric heating control prediction model. Then, it inputs real-time temperature data and real-time heating power data into the ship pipeline electric heating control prediction model to obtain electric heating control prediction data. Combined with real-time temperature data, it calculates the electric heating demand power prediction value to obtain the electric heating demand power prediction value. Then, based on the electric heating demand power prediction value, it adjusts the electric heating process of the ship pipeline to achieve accurate electric heating of the ship pipeline.

[0010] This invention trains an initial neural network model using historical temperature and heating power data, enabling the resulting ship pipeline electric heating control prediction model to fully explore and couple the relationship between historical temperature and heating power data. This allows the model to accurately predict ship pipeline electric heating control. Then, by inputting real-time temperature and heating power data into the model, the resulting electric heating control prediction data can dynamically identify and integrate these data, improving the accuracy of the predictions. Finally, the predicted electric heating power demand is calculated using the real-time temperature data, thereby regulating the ship pipeline electric heating process. This not only avoids the poor heating and insulation effects caused by relying solely on constant power heating, but also reduces electric heat tracing energy consumption and improves the accuracy and reliability of ship pipeline heating, ensuring the normal operation of the ship in polar environments, especially in the face of complex and variable temperature fluctuations.

[0011] As a preferred embodiment, the step of acquiring historical temperature data, historical heating power data, and an initial neural network model, and training the initial neural network model based on the historical temperature data and the historical heating power data to obtain a ship pipeline electric heating control prediction model includes:

[0012] Acquire historical temperature data, historical heating power data, and an initial neural network model, wherein the historical temperature data includes: historical ambient temperature data, historical external pipe temperature data, and historical internal pipe temperature data;

[0013] The historical ambient temperature data, historical pipeline external temperature data, historical pipeline internal temperature data, and historical heating power data are processed according to a preset data processing method to obtain a ship pipeline heating control training sequence.

[0014] The training sequence for ship pipeline heating control is input into the initial neural network model, and the initial neural network model is trained based on a preset neural network model training method to obtain a prediction model for ship pipeline electric heating control.

[0015] This preferred solution integrates historical temperature and heating power data from multiple dimensions and processes the data according to a preset data processing method to obtain a training sequence for ship pipeline heating control. Then, based on this training sequence, the initial neural network model is trained, enabling the ship pipeline electric heating control prediction model to fully integrate the relationship between various temperature factors and heating power in the polar environment. This more closely reflects the complex temperature conditions of ship pipelines in actual polar environments, fully considering the impact of multiple factors on the heating effect. This allows the ship pipeline electric heating control prediction model to more accurately predict ship pipeline electric heating control. Furthermore, using this model for electric heating control prediction can eliminate control delays caused by temperature lag, thereby further improving the accuracy and reliability of ship pipeline electric heating.

[0016] As a preferred embodiment, the step of processing the historical ambient temperature data, historical pipeline external temperature data, historical pipeline internal temperature data, and historical heating power data according to a preset data processing method to obtain a ship pipeline heating control training sequence includes:

[0017] The historical ambient temperature data is normalized based on a preset data processing method to obtain a historical ambient temperature training set.

[0018] The historical pipeline external temperature data is normalized based on a preset data processing method to obtain a training set of historical pipeline external temperature.

[0019] The historical pipeline internal temperature data is normalized based on a preset data processing method to obtain a training set of historical pipeline internal temperature.

[0020] The historical heating power data is normalized based on a preset data processing method to obtain a historical heating power training set.

[0021] The training sequence for ship pipeline heating control is determined based on the historical ambient temperature training set, the historical pipeline external temperature training set, the historical pipeline internal temperature training set, and the historical heating power training set.

[0022] This preferred solution normalizes various data types, mapping historical ambient temperature data, historical external pipeline temperature data, historical internal pipeline temperature data, and historical heating power data to a unified dimensional space. This improves the accuracy of the subsequently constructed ship pipeline heating control training sequence, enhances the quality and efficiency of the initial neural network model training, avoids the adverse effects of excessive data differences on model training, and ultimately enables the trained ship pipeline electric heating control prediction model to have stronger generalization ability and adaptability when facing different temperature data, thereby improving the accuracy and reliability of ship pipeline electric heating.

[0023] As a preferred embodiment, the step of calculating the predicted electric heating power demand based on the electric heating control prediction data and the real-time temperature data, and adjusting the electric heating process of the ship's pipeline according to the predicted electric heating power demand, includes:

[0024] The electric heating control prediction data includes: PID controller proportional parameters, PID controller integral parameters, and PID controller derivative parameters;

[0025] Obtain the rated temperature data of the ship's pipelines, and determine the real-time temperature difference data based on the rated temperature data of the ship's pipelines and the real-time temperature data;

[0026] Based on the real-time temperature difference data, the proportional parameters, integral parameters, and derivative parameters of the PID controller, the predicted power demand for electric heating is calculated, and the electric heating process of the ship's pipeline is adjusted according to the predicted power demand.

[0027] This preferred solution introduces PID control, dynamically coupling PID control with real-time temperature difference data. By calculating the predicted power demand for electric heating, the electric heating process of the ship's pipelines is adjusted. The PID control-based adjustment method can quickly and accurately respond to the temperature change demand of the ship's pipelines, thereby realizing the dynamic adjustment of the electric heating of the ship's pipelines. This not only avoids the poor heating and insulation effect caused by simply relying on constant power heating, but also reduces the energy consumption of electric heat tracing and improves the accuracy and reliability of heating the ship's pipelines, ensuring the normal operation of the ship in the polar environment, in response to the complex and variable temperature fluctuations during polar voyages.

[0028] As a preferred embodiment, the step of calculating the predicted electric heating power demand based on the real-time temperature difference data, the proportional parameter, the integral parameter, and the derivative parameter of the PID controller, and adjusting the electric heating process of the ship's pipeline according to the predicted electric heating power demand includes:

[0029] The real-time temperature difference data, PID controller proportional parameters, PID controller integral parameters and PID controller derivative parameters are input into the preset electric heat tracing power demand calculation formula to obtain the predicted electric heating power demand value.

[0030] The electric heating process of the ship's pipeline is adjusted according to the predicted electric heating power demand.

[0031] The specific formula for calculating the power requirement of electric heat tracing is as follows:

[0032]

[0033] P heat (t) represents the predicted power demand for electric heating at time t, K p K is the proportional parameter of the PID controller. i K is the integral parameter of the PID controller. d Here, e(t) represents the differential parameter of the PID controller, and e(t) represents the real-time temperature difference data. This is the integral of the real-time temperature difference data with respect to time t.

[0034] This preferred solution calculates the predicted power demand for electric heating using real-time temperature difference data, PID controller proportional parameters, PID controller integral parameters, and PID controller derivative parameters. This enables timely and accurate calculation of the predicted power demand for electric heating. The solution achieves intelligent calculation of the predicted power demand for electric heating using a preset formula, thereby enabling effective regulation of ship pipeline heating using PID control and improving the accuracy and reliability of ship pipeline heating.

[0035] As a preferred embodiment, after adjusting the electric heating process of the ship's pipeline based on the predicted electric heating power demand, the method further includes:

[0036] Obtain the pipe length and pipe radius data of the ship's pipeline;

[0037] The real-time temperature data includes: real-time ambient temperature data and real-time pipe internal temperature data;

[0038] The heat loss power of the ship's pipeline is calculated based on the real-time ambient temperature data, real-time internal pipeline temperature data, pipeline length data, and pipeline radius data.

[0039] The energy-saving rate of the ship's pipeline is calculated based on the heat loss power.

[0040] The prediction model for the control of electric heating in the ship's pipeline is optimized based on the energy saving rate.

[0041] This preferred solution calculates the heat loss power of the ship's pipeline using real-time ambient temperature data, real-time internal pipeline temperature data, pipeline length data, and pipeline radius data. It then calculates the energy-saving rate of the ship's pipeline and optimizes the prediction model for electric heating control of the ship's pipeline based on the energy-saving rate. This allows the prediction model to continuously adapt to the actual operating conditions and environmental changes of the ship's pipeline, further improving the model's prediction accuracy and applicability, thereby enhancing the accuracy and reliability of subsequent heating of the ship's pipeline.

[0042] Accordingly, this invention provides a marine pipeline electric heating system, including: a sensor module, a control cabinet, heating cables, marine pipelines, and a switch module;

[0043] The heating cable is used to electrically heat the ship's pipeline;

[0044] The control cabinet is used to acquire historical temperature data, historical heating power data, and an initial neural network model, and to train the initial neural network model based on the historical temperature data and the historical heating power data to obtain a prediction model for ship pipeline electric heating control.

[0045] The control cabinet is also used to acquire real-time temperature data and real-time heating power data collected by the sensor module, and input the real-time temperature data and real-time heating power data into the ship pipeline electric heating control prediction model to obtain electric heating control prediction data.

[0046] The control cabinet is also used to generate switch drive commands based on the electric heating control prediction data and the real-time temperature data, so that the switch module can adjust the electric heating process of the ship's pipeline according to the switch drive commands.

[0047] This system electrically heats the ship's pipelines using heating cables. A sensor module collects real-time temperature and heating power data. The control cabinet constructs a predictive model for the ship's pipeline electric heating control, calculates predicted electric heating control data, and generates switch drive commands to adjust the electric heating process of the ship's pipelines. This forms a ship's pipeline electric heating system capable of accurate control. By training an initial neural network model with historical temperature and heating power data, the resulting predictive model fully explores and couples the relationship between historical temperature and heating power data, enabling accurate control of the ship's pipeline electric heating. The system predicts the control of electric heating in ship pipelines. Real-time temperature and heating power data are then input into the prediction model, enabling dynamic identification and fusion of these data. This improves the accuracy of the predicted electric heating control data. The predicted power demand for electric heating is then calculated using the real-time temperature data, allowing for the regulation of the ship's pipeline electric heating process. This approach not only avoids the poor heating and insulation effects caused by relying solely on constant power heating but also addresses the complex and variable temperature fluctuations during polar voyages, reducing energy consumption for electric heat tracing, improving the accuracy and reliability of ship pipeline heating, and ensuring the normal operation of the ship in polar environments.

[0048] As a preferred embodiment, the ship's pipeline electric heating system further includes: a power supply, a power cable, and a power junction box;

[0049] The power junction box is evenly installed on the heating cable and is used to supply power to the heating cable.

[0050] The power source is electrically connected to the power junction box via the power cable, and the power source is used to supply power to the power junction box.

[0051] This preferred solution uses a combination of distributed power junction boxes and power cables to achieve uniform power distribution and redundant backup for the heating cables. This avoids the problems of localized overheating or insufficient heating of pipelines caused by uneven power supply in traditional electric heating methods, thus ensuring the uniformity and accuracy of heating the ship's pipelines.

[0052] As a preferred option, the heating cable is a self-regulating heating cable.

[0053] This preferred solution uses a heating cable with a self-regulating heating cable. Through the automatic temperature limiting characteristic of the self-regulating heating cable, energy waste caused by overheating can be effectively avoided, thus better adapting to the complex and variable temperature fluctuations in the polar environment. It can also reduce the damage of high temperature to ship pipelines and achieve reliable heating of ship pipelines.

[0054] As a preferred embodiment, the sensor module, heating cable, marine pipeline, and power junction box are all wrapped with an insulating and heat-insulating layer.

[0055] This preferred solution effectively reduces ineffective heat exchange between the pipeline and the environment by wrapping the sensor module, heating cable, ship pipeline, and power junction box with an insulating heat insulation layer. This further reduces heat loss in the ship pipeline electric heating system, improves the thermal efficiency of the ship pipeline electric heating process, and ensures that more of the heat generated by the heating cable is used to heat the ship pipeline, thereby improving the accuracy and efficiency of ship pipeline electric heating. Attached Figure Description

[0056] Figure 1 A flowchart illustrating the steps of a ship pipeline electric heating method provided in an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of a ship pipeline electric heating system provided in an embodiment of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1

[0060] Please refer to Figure 1 , Figure 1 The flowchart of a method for electrically heating a ship pipeline provided in an embodiment of the present invention includes steps S101 to S103.

[0061] Step S101: Obtain historical temperature data, historical heating power data, and an initial neural network model, and train the initial neural network model based on the historical temperature data and the historical heating power data to obtain a prediction model for ship pipeline electric heating control.

[0062] In this embodiment, the step of acquiring historical temperature data, historical heating power data, and an initial neural network model, and training the initial neural network model based on the historical temperature data and the historical heating power data to obtain a ship pipeline electric heating control prediction model, includes:

[0063] Acquire historical temperature data, historical heating power data, and an initial neural network model, wherein the historical temperature data includes: historical ambient temperature data, historical external pipe temperature data, and historical internal pipe temperature data;

[0064] The historical ambient temperature data, historical pipeline external temperature data, historical pipeline internal temperature data, and historical heating power data are processed according to a preset data processing method to obtain a ship pipeline heating control training sequence.

[0065] The training sequence for ship pipeline heating control is input into the initial neural network model, and the initial neural network model is trained based on a preset neural network model training method to obtain a prediction model for ship pipeline electric heating control.

[0066] This embodiment integrates historical temperature data and historical heating power data from multiple dimensions, and processes the data according to a preset data processing method to obtain a ship pipeline heating control training sequence. Then, based on the ship pipeline heating control training sequence, the initial neural network model is trained, so that the ship pipeline electric heating control prediction model can fully integrate the relationship between various temperature factors and heating power in the polar environment. This makes it closer to the complex temperature situation of ship pipelines in the actual polar environment, and can fully consider the influence of multiple factors on the heating effect. This allows the ship pipeline electric heating control prediction model to more accurately predict the ship pipeline electric heating control. Furthermore, by using the ship pipeline electric heating control prediction model to predict electric heating control, the control delay caused by temperature lag can be eliminated, thereby further improving the accuracy and reliability of ship pipeline electric heating.

[0067] In this embodiment, the step of processing the historical ambient temperature data, historical pipeline external temperature data, historical pipeline internal temperature data, and historical heating power data according to a preset data processing method to obtain a ship pipeline heating control training sequence includes:

[0068] The historical ambient temperature data is normalized based on a preset data processing method to obtain a historical ambient temperature training set.

[0069] The historical pipeline external temperature data is normalized based on a preset data processing method to obtain a training set of historical pipeline external temperature.

[0070] The historical pipeline internal temperature data is normalized based on a preset data processing method to obtain a training set of historical pipeline internal temperature.

[0071] The historical heating power data is normalized based on a preset data processing method to obtain a historical heating power training set.

[0072] The training sequence for ship pipeline heating control is determined based on the historical ambient temperature training set, the historical pipeline external temperature training set, the historical pipeline internal temperature training set, and the historical heating power training set.

[0073] This embodiment normalizes various data types, mapping historical ambient temperature data, historical external pipeline temperature data, historical internal pipeline temperature data, and historical heating power data to a unified dimensional space. This improves the accuracy of the subsequently constructed ship pipeline heating control training sequence, enhances the quality and efficiency of the initial neural network model training, avoids the adverse effects of excessive data differences on model training, and ultimately enables the trained ship pipeline electric heating control prediction model to have stronger generalization ability and adaptability when facing different temperature data, thereby improving the accuracy and reliability of ship pipeline electric heating.

[0074] It should be noted that this embodiment sets historical heating power data P. heat,history The initial neural network model is an LSTM model, and the historical temperature data includes: historical ambient temperature data T env,history Historical pipeline external temperature data T pipe,history and historical pipe internal temperature data T in,history The preset data processing method is Min-Max normalization; Min-Max normalization is a commonly used data preprocessing method used to linearly map data to a specific range (such as [0,1] or [-1,1]); LSTM model refers to Long Short-Term Memory (LSTM), a special type of recurrent neural network (RNN) that effectively captures long-term dependencies in time series by introducing gating mechanisms and cell states, specifically forget gate, input gate, candidate state, cell state update, output gate, and hidden state output; the expression for the forget gate is f. t =σ(W f ·[h t；1 ,x t ]+b f The expression for the input gate is i. t =σ(W i ·[h t；1 ,x t ]+b i The candidate state is expressed as follows: The cell state update expression is The output gate expression is o t =σ(W o ·[h t；1 ,x t ]+b o The hidden state output expression is: In the above expression, σ is the Sigmoid function, tanh is the hyperbolic tangent function, and ° represents element-wise multiplication (Hadamard product).

[0075] In one optional embodiment, specifically, historical ambient temperature data T from the past sixty time steps (the past ten minutes) is collected. env,history Historical pipeline external temperature data T pipe,history Historical pipe internal temperature data T in,history and historical heating power data P heat,history Then, the Min-Max normalization method was used to analyze the historical ambient temperature data T. env,history Normalize to the [-1,1] interval to obtain the historical ambient temperature training set. Use the Min-Max normalization method to normalize the historical pipeline external temperature data T. pipe,history Normalize to the [-1,1] interval to obtain the historical pipeline external temperature training set. Use the Min-Max normalization method to normalize the historical pipeline internal temperature data T. in,history Normalize to the [-1,1] interval to obtain the historical pipeline internal temperature training set. Use the Min-Max normalization method to normalize the historical heating power data P. heat,history Normalization to the [-1,1] interval yields the historical heating power training set. Then, based on the time dimension, the ship's pipeline heating control training sequence [T] is determined using the historical ambient temperature training set, the historical pipeline external temperature training set, the historical pipeline internal temperature training set, and the historical heating power training set. env,history ,T in,history ,T pipe,history ,P heat,history ]; then the ship's pipeline heating control training sequence [T env,history ,T in,history ,T pipe,history ,P heat,history The input is an LSTM model, which includes an input layer, hidden layers, and an output layer. The input layer is a 4-dimensional input, used to receive the training sequence for ship pipeline heating control [T]. env,history ,T in,history ,T pipe,history ,P heat,history The data consists of four different types, and the hidden layer comprises two LSTM layers, each with 64 neurons and an activation function of tanh. The output layer outputs the optimized proportional parameter ΔK of the PID controller. p Optimized value of integral parameter ΔK for PID controller i and the optimized value of the differential parameter αK of the PID controller dThen, the loss function was set as mean squared error (MSE), the optimizer was set as Adam, the learning rate was 0.001, and the batch size was 32. The LSTM model was trained using the ship pipeline heating control training sequence. Training was terminated when the loss of the validation set (which can be obtained by partitioning the ship pipeline heating control training sequence) did not decrease for 5 consecutive rounds, thus obtaining the ship pipeline electric heating control prediction model.

[0076] Step S102: Obtain real-time temperature data and real-time heating power data, and input the real-time temperature data and real-time heating power data into the ship pipeline electric heating control prediction model to obtain electric heating control prediction data.

[0077] In one optional embodiment, real-time temperature data and real-time heating power data are acquired, i.e., real-time ambient temperature data T is acquired. env,now Real-time external temperature data of pipelines (T) pipe,now Real-time pipe internal temperature data T in,now and real-time heating power data P heat,now Then, the prediction model for ship pipeline electric heating control is input, and the optimized value αK of the PID controller proportional parameter output by the prediction model for ship pipeline electric heating control is obtained. p Optimized value of integral parameter αK for PID controller i And the optimized value of the derivative parameter ΔK of the PID controller d Then obtain the real-time PID controller proportional parameter K. p,now Real-time PID controller integral parameter K i,now and the differential parameter K of the real-time PID controller d,now (i.e., the three control parameters of the current PID controller), optimize the proportional parameter ΔK of the PID controller. p and the proportional parameter K of the real-time PID controller p,now By adding them together, we obtain the proportional parameter K of the PID controller. p ;Optimize the integral parameter value ΔK of the PID controller i and the integral parameter K of the real-time PID controller i,now By adding them together, we obtain the integral parameter K of the PID controller. i The optimized value of the differential parameter ΔK of the PID controller d and the derivative parameter K of the real-time PID controller d,now By adding them together, we can obtain the differential parameter K of the PID controller. d Therefore, based on the proportional parameter K of the PID controller p PID controller integral parameter K i PID controller derivative parameter K d Predictive data for electric heating control was obtained.

[0078] It should be noted that the proportional parameter K of the PID controller p PID controller integral parameter K i PID controller derivative parameter K d These refer to the three control parameters of a PID controller. A PID controller (Proportional-Integral-Derivative Controller) is a classic and widely used feedback controller that controls the system through three components: proportional, integral, and derivative.

[0079] Step S103: Calculate the predicted value of electric heating power demand based on the electric heating control prediction data and the real-time temperature data, and adjust the electric heating process of the ship pipeline according to the predicted value of electric heating power demand.

[0080] In this embodiment, the step of calculating the predicted electric heating power demand based on the electric heating control prediction data and the real-time temperature data, and adjusting the electric heating process of the ship's pipeline according to the predicted electric heating power demand, includes:

[0081] The electric heating control prediction data includes: PID controller proportional parameters, PID controller integral parameters, and PID controller derivative parameters;

[0082] Obtain the rated temperature data of the ship's pipelines, and determine the real-time temperature difference data based on the rated temperature data of the ship's pipelines and the real-time temperature data;

[0083] Based on the real-time temperature difference data, the proportional parameters, integral parameters, and derivative parameters of the PID controller, the predicted power demand for electric heating is calculated, and the electric heating process of the ship's pipeline is adjusted according to the predicted power demand.

[0084] This embodiment introduces PID control, dynamically coupling PID control with real-time temperature difference data. By calculating the predicted power demand for electric heating, the electric heating process of the ship's pipelines is adjusted. The adjustment method based on PID control can quickly and accurately respond to the temperature change demand of the ship's pipelines, thereby realizing the dynamic adjustment of the electric heating of the ship's pipelines. This not only avoids the poor heating and heat preservation effect caused by simply relying on constant power heating, but also reduces the energy consumption of electric heat tracing and improves the accuracy and reliability of heating the ship's pipelines, ensuring the normal operation of the ship in the polar environment, in response to the complex and variable temperature fluctuations during polar voyages.

[0085] In this embodiment, the step of calculating the predicted electric heating power demand based on the real-time temperature difference data, the proportional parameter, the integral parameter, and the derivative parameter of the PID controller, and adjusting the electric heating process of the ship's pipeline according to the predicted electric heating power demand, includes:

[0086] The real-time temperature difference data, PID controller proportional parameters, PID controller integral parameters and PID controller derivative parameters are input into the preset electric heat tracing power demand calculation formula to obtain the predicted electric heating power demand value.

[0087] The electric heating process of the ship's pipeline is adjusted according to the predicted electric heating power demand.

[0088] The specific formula for calculating the power requirement of electric heat tracing is as follows:

[0089]

[0090] P heat (t) represents the predicted power demand for electric heating at time t, K p K is the proportional parameter of the PID controller. i K is the integral parameter of the PID controller. d Here, e(t) represents the differential parameter of the PID controller, and e(t) represents the real-time temperature difference data. This is the integral of the real-time temperature difference data with respect to time t.

[0091] In an optional embodiment, the formula for calculating the real-time temperature difference data is e(t) = T set -T in (t), where T set For rated temperature data of ship pipelines, T in (t) represents the internal temperature data of the pipe at time t; the real-time internal temperature data of the pipe T in,now Substitute T in (t), and the real-time temperature difference data e(t) is calculated.

[0092] In one optional embodiment, after obtaining the predicted power demand for electric heating, the PID controller is used to adjust the electric heating process of the ship's pipeline within the next five minutes based on the proportional parameter, integral parameter, and derivative parameter of the PID controller, to ensure that the power value during the electric heating process of the ship's pipeline is the predicted power demand for electric heating.

[0093] This embodiment calculates the predicted power demand for electric heating by using real-time temperature difference data, PID controller proportional parameters, PID controller integral parameters, and PID controller derivative parameters. This enables timely and accurate calculation of the predicted power demand for electric heating. The preset formula for calculating the power demand for electric heat tracing achieves intelligent solution for the predicted power demand for electric heating. This allows for effective regulation of ship pipeline heating using PID control, improving the accuracy and reliability of ship pipeline heating.

[0094] In this embodiment, after adjusting the electric heating process of the ship's pipeline according to the predicted electric heating power demand, the method further includes:

[0095] Obtain the pipe length and pipe radius data of the ship's pipeline;

[0096] The real-time temperature data includes: real-time ambient temperature data and real-time pipe internal temperature data;

[0097] The heat loss power of the ship's pipeline is calculated based on the real-time ambient temperature data, real-time internal pipeline temperature data, pipeline length data, and pipeline radius data.

[0098] The energy-saving rate of the ship's pipeline is calculated based on the heat loss power.

[0099] The prediction model for the control of electric heating in the ship's pipeline is optimized based on the energy saving rate.

[0100] In an optional embodiment, the formula for calculating the heat loss power of the ship's pipeline is as follows:

[0101]

[0102] Among them, Q loss Where L is the heat loss power, T is the length of the ship's pipeline, and L is the length of the pipeline. in For the internal temperature data of the pipe; T env For ambient temperature data, r in r is the inner radius of the pipe. out Where is the outer radius of the pipe, k is the thermal conductivity of the insulation material, and h is the convective heat transfer coefficient.

[0103] It should be noted that overshoot refers to the situation where the control exceeds the target setpoint during PID control, which is usually caused by improper adjustment of PID parameters.

[0104] In one optional embodiment, the pipe length data, pipe radius data (including the inner and outer radii), real-time ambient temperature data, and real-time internal pipe temperature data of the ship's pipeline are obtained. Then, the thermal conductivity of the ship's pipeline insulation material is obtained (this can be found in the ship's pipeline manual, for example, polyurethane foam k = 0.035 W / (m·K)). The convective heat transfer coefficient can be directly obtained (for example, under forced convection conditions, h = 25 W / (m²·K)). 2 Substituting K)) into the above formula for calculating the heat loss power of ship pipelines, we can obtain the heat loss power of ship pipelines, and then obtain the energy saving rate of ship pipelines based on the heat loss power.

[0105] Specifically, the length of the ship's pipeline is set to L = 20m, and the outer radius of the pipeline is r. out =0.12m, pipe inner radius r in =0.1m, ambient temperature data at night is T env = -10℃, which is T during the day. env =5℃; Rated temperature data for ship pipelines T set =40℃; then calculate the heat loss power of the ship's pipelines, specifically the heat loss power at night: The heat loss power during the day is: When using PID control, there is a 20% overshoot. Under these conditions, the actual nighttime electric heating power is 3.53 × 1.2 = 4.24 kW, and the actual daytime electric heating power is 2.47 × 1.2 = 2.96 kW. Therefore, based on the heat loss power during the day and night, the average daily energy consumption using PID control can be calculated as W. PID =12×4.24+12×2.96=86.4kWh; After obtaining the electric heating control prediction data using the ship pipeline electric heating control prediction model (LSTM model), and combining it with PID control, the overshoot can be reduced to 5%; At this time, the actual nighttime electric heating power is 3.53×1.05=3.71kW, and the actual daytime electric heating power is 2.47×1.05=2.59kW; The average daily energy consumption is W LSTM =12×3.71+12×2.59=75.6kWh; Therefore, the energy saving rate is: When the energy saving rate does not meet the preset energy saving requirement (set as a threshold), the prediction model for ship pipeline electric heating control (LSTM model) is optimized based on the difference between the energy saving rate and the preset energy saving requirement. In particular, there are many mature existing technologies for optimizing the prediction model for ship pipeline electric heating control (LSTM model), so this embodiment will not elaborate further.

[0106] This embodiment calculates the heat loss power of the ship's pipeline using real-time ambient temperature data, real-time internal pipeline temperature data, pipeline length data, and pipeline radius data. It then calculates the energy-saving rate of the ship's pipeline and optimizes the prediction model for electric heating control of the ship's pipeline based on the energy-saving rate. This allows the prediction model to continuously adapt to the actual operating conditions and environmental changes of the ship's pipeline, further improving the model's prediction accuracy and applicability, thereby enhancing the accuracy and reliability of subsequent heating of the ship's pipeline.

[0107] This embodiment trains an initial neural network model using historical temperature and heating power data, enabling the resulting ship pipeline electric heating control prediction model to fully explore and couple the relationship between historical temperature and heating power data. This allows the model to accurately predict the ship pipeline electric heating control. Then, real-time temperature and heating power data are input into the model, allowing it to dynamically identify and integrate these data, improving the accuracy of the predictions. Finally, the predicted electric heating power demand is calculated using the real-time temperature data, thus regulating the ship pipeline electric heating process. This not only avoids the poor heating and insulation effects caused by relying solely on constant power heating, but also reduces energy consumption for electric heat tracing in the face of complex and variable temperature fluctuations during polar voyages, improving the accuracy and reliability of ship pipeline heating and ensuring the normal operation of the ship in polar environments.

[0108] Example 2

[0109] Please refer to Figure 2 , Figure 2 This is a schematic diagram of a ship pipeline electric heating system provided in an embodiment of the present invention. The ship pipeline electric heating system is applicable to ships and includes: a sensor module 201, a control cabinet 202, a heating cable 203, a ship pipeline 204, and a switch module 205.

[0110] The heating cable 203 is used to electrically heat the ship's pipeline 204;

[0111] The control cabinet 202 is used to acquire historical temperature data, historical heating power data and an initial neural network model, and to train the initial neural network model based on the historical temperature data and the historical heating power data to obtain a prediction model for ship pipeline electric heating control.

[0112] The control cabinet 202 is also used to acquire real-time temperature data and real-time heating power data collected by the sensor module 201, and input the real-time temperature data and real-time heating power data into the ship pipeline electric heating control prediction model to obtain electric heating control prediction data.

[0113] The control cabinet 202 is also used to generate a switch drive command based on the electric heating control prediction data and the real-time temperature data, so that the switch module 205 adjusts the electric heating process of the ship pipeline 204 according to the switch drive command.

[0114] In this embodiment, the ship pipeline electric heating system further includes: a power supply 206, a power cable 207, and a power junction box 208;

[0115] The power junction box 208 is evenly installed on the heating cable 203 and is used to supply power to the heating cable 203;

[0116] The power supply 206 is electrically connected to the power junction box 208 via the power cable 207, and the power supply 206 is used to supply power to the power junction box 208.

[0117] In an optional embodiment, the power supply 206 can be powered by the ship's generator grounding distribution board; the control cabinet 202 generates a switch drive command based on the electric heating control prediction data and the real-time temperature data, drives the switch module 205 to turn on or off, thereby controlling the power supply to the power junction box, and then controlling the power supply of the power junction box to the heating cable, and then controlling the ship's pipeline electric heating process based on controlling the power supply of the heating cable.

[0118] This embodiment uses a power supply scheme combining a distributed power junction box and a power cable to achieve uniform power distribution and redundant backup for the heating cable. This avoids the problem of local overheating or insufficient heating of the pipeline caused by uneven power supply in traditional electric heating methods, thus ensuring the uniformity and accuracy of heating the ship's pipelines.

[0119] In this embodiment, the sensor module 201 includes: a sensor junction box 2011, an ambient temperature sensor 2012, a cable limit temperature sensor 2013, a pipe temperature sensor 2014, and a heating power sensor 2015;

[0120] The sensor junction box 2011 is equipped with a temperature data transmission interface for electrical connection to the ambient temperature sensor 2012, the cable limit temperature sensor 2013, the pipe temperature sensor 2014, and the heating power sensor 2015, respectively. Figure 2 2011, 2012, 2013, 2014, 2015 shown);

[0121] The ambient temperature sensor 2012 is used to monitor the ambient temperature of the ship's pipeline 204; the cable limiting temperature sensor 2013 is used to monitor the temperature of the heating cable 203; the pipeline temperature sensor 2014 is used to monitor the external and internal temperature data of the ship's pipeline 204; and the heating power sensor 2015 is used to monitor the real-time heating power data of the heating cable 203.

[0122] The ambient temperature sensor 2012 can be installed on the deck of the ship, with a sampling interval of 10 seconds; the cable limit temperature sensor 2013 is installed inside the heating cable 203; the pipe temperature sensor 2014 is installed above the ship's pipe 204; and the heating power sensor 2015 is installed inside the heating cable 203.

[0123] In an optional embodiment, the ambient temperature sensor 2012 is specifically a PT100 platinum resistance thermometer with a range of -50℃ to 150℃, an accuracy of ±0.1℃, and IP68 protection. The pipeline temperature sensor 2014 can employ a DS18B20 digital sensor and a K-type thermocouple. When measuring the internal temperature data of the pipeline, the DS18B20 digital sensor is used, with a range of -55℃ to 125℃, an accuracy of ±0.1℃, and single-bus communication. When using the DS18B20 digital sensor, it can be inserted into the medium flow channel of the ship's pipeline after thread sealing. When measuring the external temperature data of the pipeline, the K-type thermocouple is used, with a range of -200℃ to 1250℃ and a response time of <1s. The K-type thermocouple can be fitted to the pipe wall, and thermally conductive silicone is used to enhance thermal contact. The heating power sensor 2015 can be an ELDX-8.0 sensor with a power range of 3.6KVA to 24KVA.

[0124] In an optional embodiment, the ship pipeline electric heating system further includes: an intermediate connector 209 and a terminal connector 210; the heating cable 203 and the ship pipeline 204 are connected through the intermediate connector 209; the heating cable 203 and the ship pipeline 204 are also connected through the terminal connector 210.

[0125] In an optional embodiment, the ship's pipeline electric heating system further includes an isolation transformer 211; the isolation transformer 211 is used for electrical isolation, and the isolation transformer 211 is used to prevent the heating cable 203 from leaking electricity and having low insulation due to moisture or other reasons, thereby affecting the normal operation of the ship's power grid system.

[0126] In this embodiment, the heating cable is a self-regulating heating cable.

[0127] It should be noted that self-regulating trace heating tape is an intelligent electric heating device based on the PTC (positive temperature coefficient) effect. Its internal heating matrix and thermoplastic insulation material form an independent unit, providing additional moisture protection. Its self-regulating temperature characteristic improves the safety and reliability of the product. The heating power output can also be automatically adjusted with changes in ambient temperature. Even if there is cross or overlap during installation, it will not overheat or burn out.

[0128] This embodiment uses a self-regulating heating cable. Through the automatic temperature limiting characteristics of the self-regulating heating cable, energy waste caused by overheating can be effectively avoided, thus better adapting to the complex and variable temperature fluctuations in the polar environment. It can also reduce the damage of high temperature to ship pipelines and achieve the reliability of heating ship pipelines.

[0129] In this embodiment, the sensor module, heating cable, ship pipe and power junction box are all wrapped with an insulating and heat-insulating layer 212.

[0130] This embodiment effectively reduces ineffective heat exchange between the pipeline and the environment by wrapping the sensor module, heating cable, ship pipeline, and power junction box with an insulating heat insulation layer. This further reduces heat loss in the ship pipeline electric heating system, improves the thermal efficiency of the ship pipeline electric heating process, and ensures that more of the heat generated by the heating cable is used to heat the ship pipeline, thereby improving the accuracy and efficiency of ship pipeline electric heating.

[0131] In this embodiment, the control cabinet includes: a ship pipeline electric heating control prediction model acquisition module, an electric heating control prediction data acquisition module, and a ship pipeline electric heating adjustment module;

[0132] The ship pipeline electric heating control prediction model acquisition module is used to acquire historical temperature data, historical heating power data and an initial neural network model, and to train the initial neural network model based on the historical temperature data and the historical heating power data to obtain the ship pipeline electric heating control prediction model.

[0133] The electric heating control prediction data acquisition module is used to acquire real-time temperature data and real-time heating power data, and input the real-time temperature data and real-time heating power data into the ship pipeline electric heating control prediction model to obtain electric heating control prediction data.

[0134] The ship pipeline electric heating regulation module is used to calculate the predicted value of electric heating power demand based on the electric heating control prediction data and the real-time temperature data, and to regulate the electric heating process of the ship pipeline according to the predicted value of electric heating power demand.

[0135] In this embodiment, the ship pipeline electric heating control prediction model acquisition module includes: a ship pipeline electric heating control prediction model acquisition unit;

[0136] The ship pipeline electric heating control prediction model acquisition unit is used to acquire historical temperature data, historical heating power data and an initial neural network model. The historical temperature data includes historical ambient temperature data, historical pipeline external temperature data and historical pipeline internal temperature data.

[0137] The historical ambient temperature data, historical pipeline external temperature data, historical pipeline internal temperature data, and historical heating power data are processed according to a preset data processing method to obtain a ship pipeline heating control training sequence.

[0138] The training sequence for ship pipeline heating control is input into the initial neural network model, and the initial neural network model is trained based on a preset neural network model training method to obtain a prediction model for ship pipeline electric heating control.

[0139] In this embodiment, the ship pipeline electric heating control prediction model acquisition unit includes: a ship pipeline heating control training sequence acquisition subunit;

[0140] The ship pipeline heating control training sequence acquisition subunit is used to normalize the historical ambient temperature data based on a preset data processing method to obtain a historical ambient temperature training set.

[0141] The historical pipeline external temperature data is normalized based on a preset data processing method to obtain a training set of historical pipeline external temperature.

[0142] The historical pipeline internal temperature data is normalized based on a preset data processing method to obtain a training set of historical pipeline internal temperature.

[0143] The historical heating power data is normalized based on a preset data processing method to obtain a historical heating power training set.

[0144] The training sequence for ship pipeline heating control is determined based on the historical ambient temperature training set, the historical pipeline external temperature training set, the historical pipeline internal temperature training set, and the historical heating power training set.

[0145] In this embodiment, the ship pipeline electric heating adjustment module includes: a ship pipeline electric heating adjustment unit;

[0146] In the ship pipeline electric heating regulation unit, the electric heating control prediction data includes: PID controller proportional parameters, PID controller integral parameters, and PID controller derivative parameters;

[0147] The ship pipeline electric heating adjustment unit is used to acquire the rated temperature data of the ship pipeline, and to determine the real-time temperature difference data based on the rated temperature data of the ship pipeline and the real-time temperature data.

[0148] Based on the real-time temperature difference data, the proportional parameters, integral parameters, and derivative parameters of the PID controller, the predicted power demand for electric heating is calculated, and the electric heating process of the ship's pipeline is adjusted according to the predicted power demand.

[0149] In this embodiment, the ship pipeline electric heating regulation unit includes: a ship pipeline electric heating regulation subunit;

[0150] The ship pipeline electric heating regulation subunit is used to input the real-time temperature difference data, PID controller proportional parameters, PID controller integral parameters and PID controller differential parameters into the preset electric heat tracing demand power calculation formula to obtain the electric heating demand power prediction value.

[0151] The electric heating process of the ship's pipeline is adjusted according to the predicted electric heating power demand.

[0152] The specific formula for calculating the power requirement of electric heat tracing is as follows:

[0153]

[0154] P heat (t) represents the predicted power demand for electric heating at time t, K p K is the proportional parameter of the PID controller. i K is the integral parameter of the PID controller. d Here, e(t) represents the differential parameter of the PID controller, and e(t) represents the real-time temperature difference data. This is the integral of the real-time temperature difference data with respect to time t.

[0155] This embodiment trains an initial neural network model using historical temperature and heating power data, enabling the resulting ship pipeline electric heating control prediction model to fully explore and couple the relationship between historical temperature and heating power data. This allows the model to accurately predict the ship pipeline electric heating control. Then, real-time temperature and heating power data are input into the model, allowing it to dynamically identify and integrate these data, improving the accuracy of the predictions. Finally, the predicted electric heating power demand is calculated using the real-time temperature data, thus regulating the ship pipeline electric heating process. This not only avoids the poor heating and insulation effects caused by relying solely on constant power heating, but also reduces energy consumption for electric heat tracing in the face of complex and variable temperature fluctuations during polar voyages, improving the accuracy and reliability of ship pipeline heating and ensuring the normal operation of the ship in polar environments.

[0156] In summary, this invention trains an initial neural network model using historical temperature and heating power data, enabling the resulting ship pipeline electric heating control prediction model to fully explore and couple the relationship between historical temperature and heating power data. This allows the model to accurately predict ship pipeline electric heating control. Subsequently, real-time temperature and heating power data are input into the model, allowing it to dynamically identify and integrate these data, improving the accuracy of the predictions. The predicted electric heating power demand is then calculated using the real-time temperature data, thereby regulating the ship pipeline electric heating process. This not only avoids the poor heating and insulation effects caused by relying solely on constant power heating but also reduces energy consumption for electric heat tracing in the face of complex and variable temperature fluctuations during polar voyages, improving the accuracy and reliability of ship pipeline heating and ensuring the normal operation of the ship in polar environments.

[0157] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method of electrically heating a marine pipeline, characterized by, include: Historical temperature data, historical heating power data, and an initial neural network model are acquired. The initial neural network model is then trained based on the historical temperature data and historical heating power data to obtain a predictive model for ship pipeline electric heating control. The historical temperature data includes historical ambient temperature data, historical pipeline external temperature data, and historical pipeline internal temperature data. These data are processed according to a preset data processing method to obtain a ship pipeline heating control training sequence. This training sequence is then input into the initial neural network model, and the initial neural network model is trained using a preset neural network model training method to obtain the predictive model for ship pipeline electric heating control. Acquire real-time temperature data and real-time heating power data, and input the real-time temperature data and real-time heating power data into the ship pipeline electric heating control prediction model to obtain electric heating control prediction data; The electric heating power demand prediction is calculated based on the electric heating control prediction data and the real-time temperature data, and the electric heating process of the ship's pipeline is adjusted according to the electric heating power demand prediction. The electric heating control prediction data includes: PID controller proportional parameters, PID controller integral parameters, and PID controller derivative parameters. The rated temperature data of the ship's pipeline is obtained, and real-time temperature difference data is determined based on the rated temperature data and the real-time temperature data. The electric heating power demand prediction is calculated based on the real-time temperature difference data, PID controller proportional parameters, PID controller integral parameters, and PID controller derivative parameters, and the electric heating process of the ship's pipeline is adjusted according to the electric heating power demand prediction. Specifically, the real-time temperature difference data, PID controller proportional parameters, PID controller integral parameters, and PID controller derivative parameters are input into a preset electric heat tracing power demand calculation formula to obtain the electric heating power demand prediction. The electric heating process of the ship's pipeline is adjusted according to the electric heating power demand prediction. The specific formula for calculating the power requirement of electric heat tracing is as follows: ； is a real-time temperature difference data, is a PID controller proportional parameter, is a PID controller integral parameter, is a PID controller derivative parameter, is real-time temperature difference data; is real-time temperature difference data with respect to is an integral at the time instant.

2. A method of electrically heating a marine pipeline as claimed in claim 1, characterised in that, The process of processing the historical ambient temperature data, historical pipeline external temperature data, historical pipeline internal temperature data, and historical heating power data according to a preset data processing method to obtain a ship pipeline heating control training sequence includes: The historical ambient temperature data is normalized based on a preset data processing method to obtain a historical ambient temperature training set. The historical pipeline external temperature data is normalized based on a preset data processing method to obtain a training set of historical pipeline external temperature. The historical pipeline internal temperature data is normalized based on a preset data processing method to obtain a training set of historical pipeline internal temperature. The historical heating power data is normalized based on a preset data processing method to obtain a historical heating power training set. The training sequence for ship pipeline heating control is determined based on the historical ambient temperature training set, the historical pipeline external temperature training set, the historical pipeline internal temperature training set, and the historical heating power training set.

3. The method for electric heating of ship pipelines as described in claim 1, characterized in that, After adjusting the electric heating process of the ship's pipeline based on the predicted electric heating power demand, the method further includes: Obtain the pipe length and pipe radius data of the ship's pipeline; The real-time temperature data includes: real-time ambient temperature data and real-time pipe internal temperature data; The heat loss power of the ship's pipeline is calculated based on the real-time ambient temperature data, real-time internal pipeline temperature data, pipeline length data, and pipeline radius data. The energy-saving rate of the ship's pipeline is calculated based on the heat loss power. The prediction model for the control of electric heating in the ship's pipeline is optimized based on the energy saving rate.

4. A marine pipeline electric heating system, characterized in that, The method for electrically heating a ship's pipeline according to any one of claims 1 to 3 includes: a sensor module, a control cabinet, a heating cable, a ship's pipeline, and a switch module; The heating cable is used to electrically heat the ship's pipeline; The control cabinet is used to acquire historical temperature data, historical heating power data, and an initial neural network model, and to train the initial neural network model based on the historical temperature data and the historical heating power data to obtain a prediction model for ship pipeline electric heating control. The control cabinet is also used to acquire real-time temperature data and real-time heating power data collected by the sensor module, and input the real-time temperature data and real-time heating power data into the ship pipeline electric heating control prediction model to obtain electric heating control prediction data. The control cabinet is also used to generate switch drive commands based on the electric heating control prediction data and the real-time temperature data, so that the switch module can adjust the electric heating process of the ship's pipeline according to the switch drive commands.

5. A ship pipeline electric heating system as described in claim 4, characterized in that, Also includes: Power supply, power cables and power junction boxes; The power junction box is evenly installed on the heating cable and is used to supply power to the heating cable; The power source is electrically connected to the power junction box via the power cable, and the power source is used to supply power to the power junction box.

6. A ship pipeline electric heating system as described in claim 4, characterized in that, The heating cable is a self-regulating heating cable.

7. The ship pipeline electric heating system as described in claim 5, characterized in that, The sensor module, heating cable, marine pipeline, and power junction box are all wrapped with an insulating and heat-insulating layer.

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