Intelligent heating control method for cargo holds of multi-hold chemical tanker and chemical tanker

Abstract

The application discloses an intelligent heating control method for a multi-cabin chemical ship cargo cabin and a chemical ship, and the method comprises the following steps: a global heat production optimization step, which comprises setting the outlet temperature of a main heater and the operation parameters of a main circulating pump according to the overall heat demand of all cargo cabins; and a multi-cabin dynamic heat distribution optimization step, which comprises acquiring the temperature data inside each cargo cabin in real time; calculating a dynamic demand index reflecting the instant heat demand of each cargo cabin in real time based on the temperature data of each cargo cabin; and cooperatively controlling the opening degree of an electric regulating valve arranged on the inlet branch of a heating coil of each cargo cabin based on the dynamic demand index, so as to dynamically adjust the heat medium flow to the heating coil of each cargo cabin. Through the above optimization steps, the application realizes accurate temperature control and dynamic heat adjustment based on real-time heat demand, and has the advantages of accurately controlling the temperature of the cargo cabin, dynamically optimizing heat distribution, improving energy efficiency and ensuring the safety of chemical transportation.

Description

Technical Field

[0001] This invention relates to marine thermal control systems, specifically to an intelligent heating control method for cargo holds of multi-compartment chemical tankers, the chemical tanker, and a computer-readable storage medium. Background Technology

[0002] In the transportation of liquid chemicals such as paraxylene, benzene, and methanol, which have high freezing points or are heat-sensitive, precise control of cargo hold temperature is directly related to transportation safety, cargo integrity, and the smooth implementation of loading and unloading operations. These chemicals are extremely sensitive to temperature changes. When the temperature is below the freezing point, the cargo may crystallize or completely solidify, making pumping impossible and causing serious economic losses and shipping delays. On the other hand, excessively high temperatures will significantly accelerate the volatilization process, not only causing cargo damage but also causing abnormally high vapor pressure in the holds, greatly increasing the risk of fire and explosion, and polluting the atmospheric environment.

[0003] Currently, the industry generally relies on ship steam systems for cargo hold heating, primarily employing indirect heating via steam coils. This involves high-temperature steam exchanging heat with the cargo through coils within the cargo hold, or directly injecting steam into the medium. However, steam heating has significant drawbacks: First, it suffers from low control precision and high system inertia. Because the steam temperature far exceeds the safe temperature limit for most chemicals, valve start-stop adjustments exhibit significant lag, easily leading to drastic temperature fluctuations in the cargo hold. This manifests as repeated overshooting and temperature drops, making it difficult to stabilize within a narrow safety window. This oscillating state may temporarily trigger spoilage of heat-sensitive cargo or cause high-freezing-point cargoes to periodically approach their freezing point. Second, it results in severe heat imbalance. When multiple compartments share a single heating cycle system, pipeline heat loss leads to excessive heat reception in near-end cargo holds while insufficient heat is supplied to distant cargo holds. Traditional control logic, to prevent freezing in distant holds, forces an increase in overall heating intensity. This results in the near-end compartment being in an unnecessary overheating state for extended periods, unnecessarily increasing the risk of evaporation, heat energy consumption, and overall ship safety pressure. Thirdly, the system lacks dynamic coordination capabilities. Existing controls are mostly based on single-loop or simple PID algorithms, which cannot handle the global optimization needs of a multi-variable, strongly coupled system. When different cargo compartments have opposite heat demands due to cargo characteristics or location differences (e.g., near-end compartments need cooling to prevent overheating while far-end compartments need heating to prevent condensation), traditional solutions struggle to achieve intelligent dynamic heat redistribution, relying solely on crude manual intervention, which is slow to respond and ineffective. Fourthly, energy efficiency is low. To compensate for control deficiencies, operators often adopt conservative strategies to maintain high heating intensity, resulting in significant energy waste. Although closed-loop hot water circulation alternatives have emerged in recent years, partially alleviating the problem of excessively high heat source temperatures through mild heating media, the core challenges of precise on-demand heat distribution across multiple compartments and dynamic optimization of the entire system remain unresolved, especially the inability to achieve global heat production coordination based on real-time thermal status and dynamic distribution between compartments. Therefore, existing technologies urgently need improvement to address these issues. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide an intelligent heating control method for cargo holds of multi-compartment chemical tankers, a chemical tanker and a computer-readable storage medium, which has the advantages of accurately controlling cargo hold temperature, dynamically optimizing heat distribution, improving energy efficiency and ensuring the safety of chemical transportation.

[0005] According to one aspect of the present invention, an intelligent heating control method for cargo holds of a multi-compartment chemical tanker is provided, the method comprising: The global heat production optimization steps include: setting the outlet temperature of the main heater and the operating parameters of the main circulation pump based on the overall heat demand of all cargo holds; and The multi-compartment dynamic heat distribution optimization steps include: Real-time acquisition of temperature data inside each cargo compartment; Based on the temperature data of each cargo hold, a dynamic demand index reflecting the immediate heat demand of each cargo hold is calculated in real time. Based on the dynamic demand index, the opening of the electric regulating valves located on the inlet branches of the heating coils in each cargo hold is controlled in a coordinated manner to dynamically adjust the flow rate of the heat medium to each cargo hold heating coil.

[0006] In one implementation, the method further includes a local adaptive fine-tuning step: For a single cargo hold, the internal temperature uniformity is determined based on temperature data at different locations along the vertical direction inside the hold. If the temperature difference between the area upstream of the coil and the area downstream of the coil in the cargo hold is greater than a preset value, the supply of heat medium to the cargo hold will be reduced, provided that the temperature of the area upstream of the coil is safe.

[0007] In one implementation, the dynamic demand index D i The calculation is based at least on a first factor and a second factor, where the first factor is the difference between the average temperature of the cargo in the cargo hold and the preset target temperature, and the second factor is the trend of the cargo temperature in the cargo hold, dT / dt. i .

[0008] In one implementation, the dynamic demand index D i The calculation is further based on a third factor, which is a safety penalty term; when the temperature of the cargo in the cargo hold approaches a preset safety threshold, the safety penalty term exerts an inhibitory effect on the dynamic demand index.

[0009] In one implementation, the preset safety threshold includes a high-temperature safety threshold and a low-temperature safety threshold; When the temperature of the cargo inside the cargo hold approaches or exceeds the high-temperature safety threshold, the safety penalty item reduces the dynamic demand index of the cargo hold to trigger a reduction or cut-off of the heat transfer medium supply to the cargo hold. When the temperature of the cargo inside the cargo hold approaches or falls below the aforementioned low-temperature safety threshold, the safety penalty increases the dynamic demand index of the cargo hold to trigger an increase in the supply of heat medium to the cargo hold.

[0010] In one implementation, the coordinated control of the opening degree of the electrically adjustable valves located on the inlet branches of the heating coils in each cargo hold, based on the dynamic demand index, specifically includes: The dynamic demand indices calculated for all cargo holds are normalized to obtain the flow allocation weights for each cargo hold. Based on the flow distribution weights, control commands are generated for the opening of the electric regulating valves in each cargo hold, wherein cargo holds with higher dynamic demand indices receive larger valve openings, and cargo holds with lower dynamic demand indices receive smaller valve openings.

[0011] In one implementation, setting the outlet temperature of the main heater and the operating parameters of the main circulation pump based on the overall heat demand of all cargo holds specifically includes: Obtain the total heat demand of all cargo holds, the efficiency curve of the main heater, and the ambient temperature parameters. The optimal outlet temperature setpoint of the main heater and the frequency setpoint of the main circulation pump for the future preset time period are obtained by using a model predictive control algorithm.

[0012] In one implementation, the model predictive control algorithm is executed in a preset predictive time domain on a rolling basis; in each control cycle, the central controller performs the following steps: Obtain the current system status and the predicted environmental temperature sequence within the predicted time domain; Based on the system thermodynamic model, the evolution of the future system state under different control inputs is predicted; Find the sequence of control variables that optimizes the objective function; The first control quantity in the control quantity sequence, namely the outlet temperature setpoint of the main heater and the frequency setpoint of the main circulation pump, is sent to the actuator. The prediction time domain ranges from 30 minutes to 2 hours, and the control period ranges from 1 minute to 5 minutes.

[0013] According to another aspect of the present invention, a chemical tanker capable of multi-compartment heating is provided, comprising: The hull includes multiple individually compartmented chemical cargo holds; Heating coils, wherein the heating coils are coiled inside or on the bulkhead of the chemical cargo tank, for transporting a heat transfer fluid from a heat source; and A heating control system, installed within the hull, controls the opening degree of the heating coils. The heating control system includes: The central controller is configured to execute global heat production optimization steps to set the outlet temperature of the main heater and the operating parameters of the main circulation pump; Multiple compartment control units, each compartment control unit corresponding to a cargo compartment, and including a temperature sensor network for monitoring the internal temperature of the cargo compartment, and an electric regulating valve located on the inlet branch of the heating coil of the cargo compartment; The central controller is further configured to perform a multi-compartment dynamic heat distribution optimization step, which includes: Based on the temperature sensor network data of each cargo compartment, a dynamic demand index reflecting the real-time heat demand of each cargo compartment is calculated in real time. Based on the dynamic demand index, coordinated control commands are generated for the electric regulating valves corresponding to each cargo compartment to dynamically adjust the flow rate of heat medium to the heating coils of each cargo compartment.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction, which, when executed on an intelligent heating control system for a multi-compartment chemical tanker cargo hold, causes the intelligent heating control system to perform operations as described in any of the preceding embodiments of the intelligent heating control method.

[0015] This invention provides an intelligent heating control method, system, and computer-readable storage medium for multi-compartment chemical tanker cargo holds. Through global heat production optimization and multi-compartment dynamic heat distribution optimization steps, it achieves precise temperature control and dynamic heat regulation based on real-time heat demand. It has the advantages of precise control of cargo hold temperature, dynamic optimization of heat distribution, improved energy efficiency, and ensuring the safety of chemical transportation.

[0016] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1A flowchart of a first embodiment of the intelligent heating control method for multi-compartment chemical tanker cargo holds of the present invention is shown.

[0018] Figure 2 A flowchart of another embodiment of the intelligent heating control method of the present invention is shown.

[0019] Figure 3 A flowchart illustrating the global heat production optimization steps of the intelligent heating control method of the present invention is shown.

[0020] Figure 4 The flowchart shows the specific sub-steps of step 112 in the intelligent heating control method of the present invention.

[0021] Figure 5 A structural schematic diagram of an embodiment of the chemical tanker with multi-compartment heating according to the present invention is shown. Detailed Implementation

[0022] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0023] For ease of understanding, the following explains some key terms in this embodiment: Multi-compartment chemical tanker cargo holds refer to independent storage spaces within a ship used to transport a variety of liquid chemicals. These cargo holds typically hold temperature-sensitive cargo or cargo with specific freezing points, and precise temperature control within them is crucial for ensuring cargo quality and transport safety.

[0024] The main heater is the core equipment that provides heat energy for the entire heating system, and its outlet temperature directly affects the initial heat level of the heat transfer medium.

[0025] The main circulation pump is responsible for driving the heat medium to circulate throughout the heating pipeline system. Its operating parameters (such as speed or frequency) determine the overall flow rate and circulation efficiency of the heat medium.

[0026] Cargo hold temperature data refers to the real-time temperature information of the cargo or cabin environment collected by sensors installed in each cargo hold. This data forms the basis for assessing the immediate thermal requirements of the cargo hold.

[0027] Heating coils are piping systems installed inside or outside cargo holds for the flow of heat transfer fluid to exchange heat with the cargo. The heat transfer fluid transfers heat to the cargo through the coils, thereby raising or maintaining the cargo temperature.

[0028] The electric regulating valve is an automated control element installed on the inlet branch of each cargo hold heating coil. By adjusting its opening degree, the flow rate of the heat medium to the corresponding cargo hold heating coil can be precisely controlled.

[0029] Heat transfer fluid flow rate refers to the volume or mass of heat transfer fluid flowing through the heating coil per unit time. Adjusting the heat transfer fluid flow rate is a key means of achieving precise control over the heat supply to the cargo hold.

[0030] Figure 1 A flowchart illustrating a first embodiment of the intelligent heating control method for multi-compartment chemical tanker cargo holds according to the present invention is shown. This method is executed by an intelligent heating control system for multi-compartment chemical tanker cargo holds. Figure 1 As shown, the method includes the following steps: Step 110: Global heat production optimization step, which includes: setting the outlet temperature of the main heater and the operating parameters of the main circulation pump based on the overall heat demand of all cargo holds.

[0031] In the global heat production optimization step, it is necessary to set the outlet temperature of the main heater and the operating parameters of the main circulation pump based on the overall heat demand of all cargo holds.

[0032] For example, one approach is to estimate the overall heat demand based on the simple average difference between the target temperature and the actual temperature of all cargo holds, using human experience or a pre-set fixed curve, and then manually or semi-automatically adjust the outlet temperature of the main heater and the operating frequency of the main circulation pump accordingly.

[0033] Another approach is for the system to periodically aggregate the set temperature and current temperature of each cargo hold, calculate an overall heat deficit, and then determine the required outlet temperature of the main heater and the rotational speed of the main circulation pump through table lookup or simple proportional control logic. These methods aim to ensure that the entire heating system can provide sufficient heat to meet the combined needs of all cargo holds, avoiding both insufficient and excessive heat supply.

[0034] Step 120: Multi-compartment dynamic heat distribution optimization step, which includes: Step 121: Acquire real-time temperature data inside each cargo compartment.

[0035] Specifically, one or more temperature sensors can be installed inside each cargo hold. These sensors are configured to periodically measure the temperature of the cargo or the air in the hold and transmit this data to a central controller. For example, a temperature sensor could be installed at the center of each cargo hold to obtain the average temperature information for that hold. Alternatively, a temperature sensor could be installed in the bottom area of ​​the cargo hold to monitor the temperature of the cargo in the areas most prone to condensation. This real-time temperature data forms the basis for subsequent assessments of the immediate thermal requirements of each cargo hold.

[0036] Step 122: Based on the temperature data of each cargo hold, calculate the dynamic demand index that reflects the real-time heat demand of each cargo hold.

[0037] Based on the temperature data of each cargo hold, a dynamic demand index reflecting the immediate heat demand of each hold needs to be calculated in real time. For example, the difference between the current temperature and a preset target temperature of each cargo hold can be used as its dynamic demand index. When the cargo hold temperature is lower than the target temperature, the difference is positive, indicating that heating is needed; when the cargo hold temperature is higher than the target temperature, the difference is negative, indicating that heating needs to be reduced. Alternatively, the absolute difference between the current temperature and the target temperature can be considered and multiplied by a preset fixed weighting coefficient to obtain the dynamic demand index. These calculation methods aim to quantify the urgency of heat demand in each cargo hold.

[0038] Step 123: Based on the dynamic demand index, coordinate the opening of the electric regulating valves installed on the inlet branches of the heating coils in each cargo hold to dynamically adjust the flow rate of the heat medium to each cargo hold heating coil.

[0039] Specifically, based on the dynamic demand index, the opening of the electrically controlled regulating valves located on the inlet branches of the heating coils in each cargo hold needs to be coordinated to dynamically adjust the flow of heat medium to the heating coils in each cargo hold. Specifically, the opening command of the corresponding electrically controlled regulating valve can be directly generated based on the dynamic demand index calculated for each cargo hold. For example, the cargo hold with a higher dynamic demand index has its corresponding electrically controlled regulating valve opening set to be larger, thus allowing more heat medium to flow into the heating coils of that cargo hold. Conversely, the cargo hold with a lower dynamic demand index has its electrically controlled regulating valve opening set to be smaller, reducing the flow of heat medium. This coordinated control method ensures that heat is distributed according to the actual needs of each cargo hold, avoiding uneven heat distribution between different cargo holds.

[0040] In summary, compared with traditional heating control methods, this embodiment of the invention introduces a global heat production optimization step, which can dynamically set the outlet temperature of the main heater and the operating parameters of the main circulation pump according to the overall heat demand of all cargo compartments.

[0041] For example, suppose a multi-compartment chemical tanker has three independent cargo holds, labeled Cargo Hold A, Cargo Hold B, and Cargo Hold C. Cargo Hold A contains chemicals with a high freezing point, with a target temperature set at 35°C; Cargo Hold B contains chemicals with a more heat-sensitive nature, with a target temperature set at 25°C; and Cargo Hold C contains chemicals with a lower freezing point, with a target temperature set at 20°C. During the voyage, due to changes in the external ambient temperature, differences in heat dissipation conditions in each cargo hold, and variations in the initial temperature of the cargo, the actual temperatures in each cargo hold deviate from the target values ​​to varying degrees.

[0042] In the above example, by employing the method of this invention, the system does not simply set the main heater to a fixed high temperature, but adjusts it according to the combined needs of cargo holds A, B, and C. This contrasts with traditional systems that rely solely on experience or fixed parameters, effectively avoiding excessive or insufficient heat production, thereby significantly reducing overall energy consumption.

[0043] This invention achieves precise, on-demand heat distribution among cargo holds through a multi-compartment dynamic heat distribution optimization step. In traditional systems, heat distribution is often uneven, leading to overheating in near-end cargo holds and insufficient heating in far-end cargo holds. However, this method acquires real-time temperature data from each cargo hold and calculates a dynamic demand index based on this data. For example, in this case, cargo hold C has a higher dynamic demand index due to its significantly lower temperature. Therefore, the system can collaboratively control the opening of the electric regulating valves on the inlet branches of the heating coils in each cargo hold, dynamically adjusting the heat transfer medium flow to the cargo hold with the most demand. This refined distribution mechanism, compared to the coarse distribution method of traditional systems that struggles to cope with differentiated needs, greatly improves the uniformity and control accuracy of heat distribution, effectively solving the problem of uneven heat distribution.

[0044] Furthermore, the embodiments of this invention possess the ability to collaboratively address dynamic risks. In traditional systems, when conflicting heating demands arise in different cargo holds, intelligent coordination is often difficult. This method, however, through real-time calculation of the dynamic demand index and coordinated control of electrically controlled valves, can quickly respond to immediate changes in each cargo hold. For example, when cargo hold A needs heating while cargo hold B needs cooling, the system can simultaneously adjust the valve openings of both cargo holds, achieving intelligent heat redistribution without relying on manual intervention. This allows the system to flexibly adapt to complex operating conditions and unforeseen circumstances, effectively overcoming the limitations of traditional methods in collaboratively addressing dynamic risks.

[0045] In summary, this intelligent heating control method, through its innovative technical concept of combining global optimization of heat production with dynamic heat distribution, achieves precise, uniform, safe, and efficient control of the heating process in the cargo holds of multi-compartment chemical tankers. It significantly improves the system's intelligence level and operational efficiency, and overcomes the problems of low control precision, uneven heat distribution, inability to coordinate dynamic risks, and high energy consumption in existing technologies.

[0046] Figure 2 A flowchart of another embodiment of the intelligent heating control method of the present invention is shown. The present invention further proposes an intelligent heating control method, which includes a local adaptive fine-tuning step. Its function is to finely manage the temperature distribution inside a single cargo compartment to solve the problem of uneven temperature in the vertical direction. Specifically, it includes: Step 131: For a single cargo hold, determine the internal temperature uniformity based on temperature data from different locations in the vertical direction.

[0047] As the heat exchange occurs in the coils, which typically flow from the bottom to the top of the cargo hold, some heat is lost downstream, leading to variations in temperature and creating vertical temperature differences within the cargo hold. This can be addressed by deploying a multi-layered temperature sensor array within the cargo hold or by using infrared thermal imaging to scan the interior and acquire temperature data at different heights. The emphasis on temperature data from different vertical locations highlights the data source and dimensions, as this data forms the basis for assessing the temperature uniformity within the cargo hold. This data can be obtained by installing temperature sensors at multiple vertical heights (bottom, middle, top, etc.) or by periodically measuring using a movable temperature probe.

[0048] Determining the internal temperature uniformity is a decision-making process that identifies whether temperature gradients exist within the cargo hold, particularly where the temperature is lower at the bottom or higher at the top. This determination can be made by setting an allowable temperature gradient threshold; when the actual gradient exceeds this threshold, non-uniformity is considered to exist. Alternatively, it can be determined by comparing the deviations of the bottom temperature from the target temperature, and the top temperature from the target temperature.

[0049] Step 132: If the temperature difference between the area corresponding to the upstream position of the coil in the cargo hold and the area corresponding to the downstream position of the coil in the cargo hold is greater than a preset value, then reduce the supply of heat medium to the cargo hold while ensuring the safety of the temperature of the area corresponding to the upstream position of the coil in the cargo hold.

[0050] The aforementioned adjustment actions are subject to constraints. Their purpose is to address situations where the upstream temperature of the coil is excessively adjusted while the downstream temperature fails to reach the ideal state. By reducing the input of the heat transfer medium, the heat exchange at the upstream temperature is reduced, while simultaneously avoiding the negative impact of insufficient adjustment at the bottom temperature. This can be achieved by sending a command to the control system to reduce the opening of the control valve, thereby decreasing the heat transfer medium flow; or by temporarily shutting off the heat transfer medium supply to the cargo compartment and restoring the supply when the temperature difference approaches the safe lower limit.

[0051] In addition, to further address the issue of excessive temperature difference between the bottom and top, some implementations may incorporate a branch loop to the coil, connecting the downstream and upstream sides of the coil via a branch pipe and valve pump system. This allows a portion of the heat transfer medium from the downstream side of the coil to flow back to the upstream side. When the temperature difference between the area corresponding to the upstream position of the coil and the area corresponding to the downstream position is determined to be significantly greater than a preset value, such as exceeding 20% ​​or 30%, the DC loop is controlled to redirect the downstream heat transfer medium back upstream. This reduces the temperature difference between the heat transfer medium upstream of the hot coil and the cargo in the hold, decreasing upstream heat exchange while increasing downstream heat exchange, thus resulting in a more uniform temperature within the individual cargo hold.

[0052] Through the above technical solution, this invention effectively solves the problem of uneven vertical temperature within a single cargo hold. This local adaptive fine-tuning step, by real-time monitoring and judgment of the temperature at different locations vertically within the cargo hold, can accurately identify specific situations where the bottom temperature is too low or the top temperature is too high. By specifically increasing the bottom heat transfer fluid flow rate or reducing the top heat transfer fluid supply while ensuring bottom safety, the overall temperature distribution of the cargo within the hold becomes more uniform, preventing localized areas from solidifying due to excessively low temperatures or evaporating due to excessively high temperatures, significantly improving cargo quality and transportation safety. This refined local control, combined with global heat production optimization and multi-hold dynamic heat distribution optimization, jointly constructs a more complete and efficient intelligent heating control system, ensuring precise temperature control of temperature-sensitive or high-freezing-point cargoes during chemical tanker transportation, reducing operational risks and energy consumption.

[0053] In some implementations, the calculation of the dynamic demand index may rely solely on a single temperature parameter, failing to accurately capture both the static state of temperature deviation and the dynamic characteristics of its changing trend. This can lead to delayed or excessive adjustment responses, affecting the accuracy and timeliness of heat allocation. To address this, the present invention further proposes that the calculation of the dynamic demand index Di is based at least on a first factor and a second factor. The first factor is the difference between the average temperature of the cargo in the cargo hold and a preset target temperature, and the second factor is the changing trend of the cargo temperature in the cargo hold, dT / dti.

[0054] The dynamic demand index Di is a comprehensive indicator used to quantify the immediate heat demand of each cargo compartment. Its calculation results serve as the basis for heat transfer fluid flow allocation and guide the adjustment of the opening of the electric regulating valves. This index can be calculated using various algorithms, such as weighted summation, fuzzy logic reasoning, or neural network models.

[0055] The first factor is the difference between the average temperature of the cargo in the cargo hold and the preset target temperature. Its function is to reflect the static deviation between the current cargo temperature and the desired temperature, providing a basis for real-time correction of temperature errors. The average temperature of the cargo in the cargo hold can be obtained by deploying multiple temperature sensors within the cargo hold, collecting temperature data from multiple points, and then averaging the data. The preset target temperature can be set according to the specific characteristics of the chemicals being transported, such as freezing point and optimal storage temperature range, and can be dynamically adjusted according to changes in the actual transportation stage or external environmental conditions.

[0056] The second factor is the temperature change trend of the cargo in the hold, dT / dti. Its function is to reflect the rate and direction of temperature change over time, thereby predicting the future direction of temperature evolution and enabling pre-regulation. This trend can be obtained through differential calculations or regression analysis of historical temperature data; for example, the average rate of temperature change over a recent period (such as the past 5 or 10 minutes) can be calculated. Alternatively, state estimation algorithms such as Kalman filtering, combined with temperature measurements and system models, can provide a more accurate estimate of the temperature change trend.

[0057] The present invention incorporates the first and second factors into the calculation of the dynamic demand index Di, enabling the index to more comprehensively and accurately reflect the real-time heat demand of each cargo hold. In the multi-hold dynamic heat distribution optimization step, after the system acquires the temperature data inside each cargo hold in real time, it no longer judges based solely on a single temperature point or simple deviation, but simultaneously assesses the difference between the current average cargo temperature and the preset target temperature (the first factor), and whether the cargo temperature is rapidly decreasing, slowly increasing, or remaining stable (the second factor). This comprehensive consideration allows the dynamic demand index Di to more accurately reflect the real-time heat demand of the cargo holds.

[0058] For example, even if the current temperature has not yet reached the low-temperature threshold, if the temperature shows a clear downward trend, the dynamic demand index Di will increase in advance, prompting the electric regulating valve on the inlet branch of the cargo hold heating coil to open earlier, increasing the heat transfer medium flow rate, thereby achieving predictive control and preventing the temperature from falling below the safety line. Conversely, if the temperature is slightly higher than the preset target temperature but shows a downward trend, it may not be necessary to immediately reduce the heat transfer medium supply, avoiding over-adjustment. This comprehensive assessment based on static deviation and dynamic trend makes the subsequent coordinated control of the opening of the electric regulating valve on the inlet branch of each cargo hold heating coil more accurate and timely, effectively avoiding the problems of adjustment lag or over-adjustment caused by incomplete information in traditional methods, thus more effectively and dynamically adjusting the heat transfer medium flow rate to each cargo hold heating coil.

[0059] As a specific implementation method, the dynamic demand index Di can be calculated using a weighted summation. For example, for a cargo hold with a preset target temperature of 30℃, the system monitors the average cargo temperature in the hold in real time to be 28℃, so the first factor is 2℃. Simultaneously, the system monitors that the cargo temperature in the hold has decreased by 0.5℃ in the past 5 minutes, meaning its temperature change trend dT / dti is -0.1℃ / min. In this case, the dynamic demand index Di can be calculated as: Di = W1 * (preset target temperature - average cargo temperature) + W2 * (-dT / dti), where W1 and W2 are preset weighting coefficients, for example, W1 can be set to 0.6 and W2 can be set to 0.4. Substituting the values, Di = 0.6 * (30 - 28) + 0.4 * (0.1) = 0.6 * 2 + 0.4 * 0.1 = 1.2 + 0.04 = 1.24. The calculated Di value will be compared and normalized with the dynamic demand indices of other cargo holds, thereby generating control commands for the opening of the corresponding electric regulating valves for that cargo hold. In this way, when the temperature drop trend is more pronounced, even if the difference between the current temperature and the target temperature is not large, the dynamic demand index Di will increase accordingly, thus triggering a more proactive supply of heat transfer fluid and achieving proactive control.

[0060] By incorporating the difference between the average cargo temperature and the preset target temperature (static deviation) and the cargo temperature change trend dT / dti (dynamic characteristics) into the calculation of the dynamic demand index Di, the index can more comprehensively and accurately reflect the real-time heat demand of the cargo hold. This effectively avoids the problems of lag or over-regulation that may result from relying solely on a single temperature parameter. For example, when the cargo temperature, although not yet below the preset target temperature, shows a rapid downward trend, the system can detect this in advance and increase the supply of heat transfer medium to prevent the temperature from falling below the safety line; conversely, when the temperature is slightly above the preset target temperature but has already begun to decline, the system can also avoid unnecessary excessive cooling. This forward-looking and refined heat demand assessment significantly improves the accuracy and timeliness of heat transfer medium flow distribution, thereby ensuring stable control of the cargo hold temperature, reducing the risk of cargo solidification or overheating, and reducing unnecessary energy consumption.

[0061] In other embodiments, the present invention further proposes that the calculation of the dynamic demand index Di is based on a third factor, which is a safety penalty term; when the temperature of the cargo in the cargo hold approaches a preset safety threshold, the safety penalty term exerts an inhibitory effect on the dynamic demand index.

[0062] The dynamic demand index Di is a key indicator for measuring the real-time heat demand of each cargo hold, and its calculation method determines the heat allocation strategy. In existing schemes, this index is mainly based on the deviation between the cargo temperature and the target temperature, as well as the temperature change trend. To more comprehensively reflect the actual heat demand of the cargo holds, this invention further incorporates an additional factor into its calculation.

[0063] It should be noted that this "further based on" can be achieved by combining the third factor with the first and second factors through a weighted summation, for example, Di = W1*F1 + W2*F2 + W3*F3, where F3 is the third factor. Alternatively, it can be achieved through logical judgment or piecewise functions, where the third factor directly or indirectly modifies the base index calculated from the first two factors when specific conditions are met.

[0064] The third factor is the safety penalty term, specifically designed to handle the safe boundary conditions of cargo hold temperature. Its core function is to intervene in the dynamic demand index Di when the cargo temperature approaches a critical point that could lead to danger or damage, thereby ensuring cargo safety. The safety penalty term can be a non-linear function, its value approaching zero or a constant when the cargo temperature is far from the safe threshold, and rapidly increasing or decreasing as the temperature approaches the safe threshold, thus significantly affecting the dynamic demand index Di. The safety penalty term can also be a binary or multi-valued switch, activated when the temperature enters a certain safety warning range, and outputting a preset penalty value or correction coefficient based on the degree to which the temperature deviates from the safe threshold.

[0065] When the cargo temperature inside the hold approaches a preset safety threshold, this describes a condition under which a safety penalty is activated or its effect is significantly amplified. The preset safety threshold is determined based on the physicochemical properties of the chemicals being transported and transportation requirements, representing the upper and lower limits of the cargo temperature. The determination of "approaching" can be achieved by setting a safety margin range; for example, when the difference between the cargo temperature and the safety threshold is less than a preset temperature difference value, the temperature is considered to be approaching the safety threshold. Alternatively, it can be determined by the rate of temperature change; for example, when the cargo temperature changes towards the safety threshold at a relatively rapid rate, it is considered to be "approaching" the safety threshold even if it has not yet entered the safety margin range.

[0066] The safety penalty term exerts an inhibitory effect on the dynamic demand index, meaning it restricts or corrects the index Di, guiding the system to take appropriate safety measures when a safety risk arises. This adjustment is not a simple increase or decrease, but rather, based on the nature of the safety risk, it prompts the heat transfer fluid flow to be adjusted towards a safer direction. When the temperature approaches the high-temperature safety threshold, the inhibitory effect manifests as a decrease in the dynamic demand index Di, thereby reducing the flow of heat transfer fluid to that cargo hold. When the temperature approaches the low-temperature safety threshold, the inhibitory effect manifests as an increase in the dynamic demand index Di, thereby increasing the flow of heat transfer fluid to that cargo hold.

[0067] The following is a concrete example. Assume a cargo hold contains chemicals with a freezing point of 13°C, a target maintenance temperature of 15°C, and a high-temperature safety threshold of 20°C. During normal operation, the dynamic demand index Di is calculated primarily based on the deviation between the current cargo temperature and the target temperature of 15°C, as well as the temperature trend. For example, when the cargo temperature is 14°C and trending downwards, Di will be higher to increase the flow of the heat transfer medium. However, if, for some reason, the cargo temperature in the hold begins to rise rapidly and approaches the high-temperature safety threshold of 20°C (e.g., reaching 19°C), a safety penalty term will be activated. This penalty term can be a non-linear function; for example, when the temperature T is in the range [15°C, 19°C], the penalty term is 0; when T is in the range [19°C, 20°C], the value of the penalty term decreases linearly or exponentially from 0, reaching its maximum negative value at T=20°C. This negative value will directly or through a multiplication factor affect the dynamic demand index Di, significantly reducing it. For example, if the original calculated value of Di is 0.8, it may be corrected to 0.2 under the effect of the safety penalty. This corrected Di value will be used to coordinate the control of the electric regulating valve of the cargo hold, significantly reducing its opening, thereby rapidly reducing the flow of heat medium to the cargo hold, and even cutting off the heat medium supply in extreme cases to prevent the cargo temperature from exceeding 20°C and avoid the risk of cargo evaporation or decomposition. Conversely, if another cargo hold is loaded with chemicals with a freezing point of -5°C, a target maintenance temperature of 0°C, and a cryogenic safety threshold of -3°C, the safety penalty will also be activated when the cargo temperature approaches -3°C (e.g., reaches -2°C). In this case, the penalty can be a non-linear function. When the temperature T is in the range of [0°C, -2°C], the penalty is 0; when T is in the range of [-2°C, -3°C], the value of the penalty increases linearly or exponentially from 0, reaching a maximum positive value at T=-3°C. This positive value will significantly increase the dynamic demand index Di, for example, correcting it from 0.3 to 0.9. This will cause the electric regulating valve to open wider, increasing the flow of the heating medium to rapidly raise the cargo temperature and prevent it from solidifying. In this way, the safety penalty can proactively and quickly adjust the dynamic demand index Di when the temperature approaches the safety limit, thereby triggering corresponding heating medium flow regulation to ensure that the cargo temperature is always maintained within the safe operating range.

[0068] It should be noted that the third factor is also related to the temperature difference between the upper and lower parts of a single cargo hold. When the difference is large, i.e., when the temperature difference within the cargo hold is significant, the third factor will increase. Simultaneously, when a branch circuit is installed within the heating coil, the opening degree of its DC circuit is also positively correlated with the third factor. This synergy ensures the safety of the cargo within the cargo hold.

[0069] Through the above technical solution, this invention effectively solves the problem that existing methods do not fully consider the risks when cargo temperature approaches a safe threshold in the calculation of dynamic demand index. By introducing a safety penalty term as the third factor in the dynamic demand index Di, and by implementing inhibitory adjustments when cargo temperature approaches a preset safe threshold, the system can achieve proactive early warning and intervention for potential safety risks. This avoids the lag in adjustments that may result from relying solely on temperature deviations and trends, thereby significantly reducing the risk of cargo deterioration, solidification, or safety accidents due to excessive temperature. This solution deeply integrates safety considerations into the decision-making logic of real-time heat distribution, enabling the entire heating control system to prioritize cargo safety while pursuing energy efficiency and temperature control accuracy, thus improving the system's reliability and robustness.

[0070] In some implementations, preset safety thresholds may include high-temperature safety thresholds and low-temperature safety thresholds. Preset safety thresholds are temperature boundaries set to ensure the safety of cargo within the cargo hold, used to determine whether the cargo temperature is in a dangerous state. Distinguishing between high-temperature and low-temperature safety thresholds allows for more refined management and response to temperature risks (i.e., overheating or overcooling) in different directions.

[0071] The aforementioned thresholds can be preset based on the specific physicochemical properties of the chemicals being transported (e.g., freezing point, boiling point, flash point, decomposition temperature, etc.), the specific requirements of the transportation contract, and relevant safety regulations, and stored in the central controller of the intelligent heating control system.

[0072] For example, for a chemical with a freezing point of 5°C, its low-temperature safety threshold can be set at 6°C to allow for a certain safety margin; for a chemical with a boiling point of 140°C, its high-temperature safety threshold can be set at 130°C to prevent overheating. Furthermore, these thresholds can also be dynamically adjusted, for example, by operators or intelligent algorithms in real time based on the specific requirements of the cargo at different stages of transportation (such as loading, sailing, and unloading), or based on changes in external environmental conditions (such as ambient air temperature and seawater temperature), to adapt to constantly changing operating conditions.

[0073] When the temperature of the cargo inside the cargo hold approaches or exceeds the aforementioned high-temperature safety threshold, the safety penalty measure reduces the dynamic demand index of the cargo hold, triggering a reduction or cutoff of the heating medium supply to that cargo hold. This technical feature aims to effectively prevent the cargo inside the cargo hold from evaporating, decomposing, or causing other safety accidents due to excessively high temperatures. When the intelligent heating control system detects that the temperature of the cargo inside the cargo hold reaches or exceeds the preset high-temperature safety threshold, the safety penalty measure will take effect, significantly reducing the dynamic demand index of the cargo hold through inhibitory regulation.

[0074] This inhibitory adjustment can be achieved in several ways. For example, the safety penalty term can be set to a negative value and directly deducted from the calculation result of the dynamic demand index, or it can be used as a multiplicative factor (with a value less than 1) in the calculation formula of the dynamic demand index to rapidly reduce its result. For instance, if the calculation formula of the dynamic demand index Di includes a safety penalty term P... safety When the cargo temperature T cargo Reaching or exceeding the high temperature safety threshold T ht At that time, P safety The value of can decrease rapidly, even approaching zero, resulting in a significant reduction in Di. This allows the system to automatically trigger a reduction or cut-off of the heat transfer medium supply to the cargo hold's heating coils, effectively controlling temperature rise and ensuring cargo safety.

[0075] This invention further proposes a method for collaboratively controlling the opening of electric regulating valves located on the inlet branches of the heating coils in each cargo hold based on a dynamic demand index. Specifically, this includes: normalizing the dynamic demand index calculated for all cargo holds to obtain the flow distribution weight for each cargo hold; and generating control commands for the opening of the electric regulating valves in each cargo hold based on the flow distribution weight, wherein cargo holds with higher dynamic demand indices receive larger valve openings, and cargo holds with lower dynamic demand indices receive smaller valve openings.

[0076] The normalization of dynamic demand indices calculated from all cargo holds involves converting data with different dimensions or numerical ranges to a unified and comparable scale. This process aims to eliminate absolute numerical differences in dynamic demand indices across cargo holds caused by variations in calculation methods, cargo characteristics, or environmental factors, allowing for comparison and allocation on a unified benchmark and ensuring fairness and accuracy in subsequent traffic allocation.

[0077] This normalization process can employ linear normalization (Min-Max Scaling), mapping all dynamic demand indices to a range of, for example, 0 to 1. That is, for each cargo hold's dynamic demand index Di, its normalized value Di_norm = (Di - min(D)) / (max(D) - min(D)), where min(D) and max(D) are the minimum and maximum values ​​of all current cargo hold dynamic demand indices, respectively. Alternatively, Z-score normalization (Standardization) can be used, transforming the data into a distribution with a mean of 0 and a standard deviation of 1, i.e., Di_norm = (Di - mean(D)) / std(D), where mean(D) is the average of all dynamic demand indices, and std(D) is the standard deviation.

[0078] Figure 3A flowchart illustrating the global heat production optimization steps of the intelligent heating control method of the present invention is shown. In some embodiments, the present invention proposes setting the outlet temperature of the main heater and the operating parameters of the main circulation pump based on the overall heat demand of all cargo holds, specifically including: Step 111: Obtain the total heat demand of all cargo holds, the efficiency curve of the main heater, and the ambient temperature parameters.

[0079] In intelligent heating control methods, obtaining the total heat demand of all cargo holds is fundamental to optimizing heat source production. This step aims to accurately assess the total heat required by the entire ship at a given moment or over a future period. This ensures that the operating parameters of the main heater and main circulation pump meet the minimum heating requirements of all cargo holds, preventing insufficient heat supply leading to cargo solidification or excessive heat causing energy waste. Specifically, this can be achieved by comprehensively calculating multiple parameters for each cargo hold, such as current temperature, target temperature, cargo type, bulkhead heat dissipation coefficient, and external ambient temperature. The individual heat demands of all cargo holds are then summed to obtain the ship's total heat demand. Alternatively, historical operating data and advanced machine learning models can be used to predict the ship's total heat demand over a future period based on current operating conditions (e.g., ambient temperature, cargo type, ship speed). The efficiency curve of the main heater is crucial data describing the variation of its energy conversion efficiency under different operating conditions.

[0080] The purpose of introducing this efficiency curve is to accurately consider the actual energy consumption characteristics of the heater during the optimization of heat source production, thereby avoiding the heater from operating in the low-efficiency range and significantly reducing energy consumption. This efficiency curve can be established based on detailed performance data provided by the manufacturer or through actual operational test data. For example, a mathematical model (such as a polynomial fitting function or lookup table) can be constructed to characterize the relationship between heater efficiency and key operating parameters such as load rate and outlet temperature.

[0081] Ambient temperature parameters refer to external environmental factors that affect heat loss from a ship's cargo hold, such as ambient air temperature, seawater temperature, wind speed, and solar radiation intensity. These parameters directly determine the heat exchange rate between the cargo hold and the external environment, thus affecting the actual heat load of the cargo hold. Obtaining these environmental parameters is crucial for more accurately predicting heat loss from the cargo hold, enabling the intelligent control system to dynamically adjust the setpoints of the main heater and main circulation pump to adapt to constantly changing environmental conditions. These environmental parameters can be collected in real time using specialized equipment such as shipboard weather stations, seawater temperature sensors, and wind speed sensors.

[0082] Step 112: Using a model predictive control algorithm, the optimal energy consumption setpoints for the main heater outlet temperature and the main circulation pump frequency are obtained within a preset time period.

[0083] The invention begins by macroscopically regulating the heat source of the entire heating system through a global heat production optimization step. Building upon this, the invention further refines the specific implementation of the global heat production optimization step to achieve optimal energy consumption in heat source production. This optimization process first acquires the overall heat demand of all cargo holds, providing fundamental load information for the heat source and ensuring that the heat supply can cover the heating needs of all cargo holds. Simultaneously, the system acquires the efficiency curve of the main heater, which reflects the energy conversion efficiency of the heater under different operating conditions, enabling the optimization algorithm to identify and avoid the heater operating in an inefficient range. Furthermore, environmental temperature parameters (such as ambient air temperature, seawater temperature, wind speed, and solar radiation intensity) are acquired in real time. These parameters effectively guide accurate prediction of heat loss from the cargo holds, allowing the system to anticipate and respond to the impact of external environmental changes on the heat load. After acquiring the aforementioned key data, the system employs a model predictive control algorithm. This algorithm utilizes the thermodynamic model of the ship's heating system, combined with the current system state and a predicted sequence of environmental parameters for a future preset time period, to proactively predict the system's behavior under different control inputs, thereby determining the setpoint for the main heater's outlet temperature and the setpoint for the main circulation pump's frequency.

[0084] Please see further. Figure 4 , Figure 4 The flowchart of the specific sub-steps of step 112 of the intelligent heating control method of the present invention is shown. In some implementations, the present invention further proposes a model predictive control algorithm to be executed in a preset prediction time domain.

[0085] In this context, the model predictive control algorithm employs a pre-defined prediction time domain rolling execution mechanism. This means that within each control cycle, the algorithm recalculates and applies only the optimal control action calculated for the current cycle to the system. The prediction time domain is then advanced by one control cycle for further optimization. This rolling execution mechanism ensures that the control system can continuously adapt to changes in the system's internal state and disturbances in the external environment, thus providing real-time feedback and adjustment capabilities. For example, an optimizer based on linear programming or quadratic programming can be used to solve a finite-time open-loop optimization problem within each control cycle; alternatively, nonlinear model predictive control (NMPC) methods can be used to process the nonlinear system model through iterative optimization algorithms to achieve rolling optimization.

[0086] In each control cycle, the central controller performs the following steps: Step 1121: Obtain the current system state and the predicted environmental temperature sequence within the predicted time domain.

[0087] Obtaining the current system state and the predicted ambient temperature sequence within the forecast time domain provides accurate initial conditions and external disturbance information for the model predictive control algorithm, thereby improving prediction accuracy. The current system state typically includes real-time measurement data such as the temperature inside the cargo hold, the temperature of the heat transfer medium, and its flow rate. The ambient temperature prediction sequence refers to the estimated value of ambient temperature (e.g., at least one of ambient air temperature, seawater temperature, wind speed, and solar radiation intensity) over a future period. For example, system state data can be obtained through real-time acquisition devices such as shipborne temperature sensor networks and flow meters; the ambient temperature prediction sequence can be obtained from external meteorological service interfaces or based on historical data from shipborne meteorological stations and short-term predictions using machine learning models.

[0088] Preferably, the ambient temperature parameter may include at least one of the following: ambient air temperature, seawater temperature, wind speed, and solar radiation intensity.

[0089] Step 1122: Based on the system thermodynamic model, predict the evolution of the future system state under different control inputs.

[0090] Predicting the evolution of the future system state under different control inputs, based on a system thermodynamic model, refers to using a model that describes the energy transfer and temperature change laws of the heating system (including the main heater, main circulating pump, cargo compartment, heat medium pipelines, etc.) to simulate the trend of the system's state change over a period of time under different main heater outlet temperature setpoints and main circulating pump frequency setpoints. This allows the controller to predict the impact of different control strategies on the future state of the system, providing a scientific basis for subsequent optimization decisions. For example, a physical model based on energy conservation and heat transfer equations, such as a lumped parameter model or a distributed parameter model, can be established to predict the system state through numerical integration; alternatively, a data-driven model, such as a neural network or support vector machine, can be used to train the model using historical operating data to predict system behavior.

[0091] Step 1123: Solve for the sequence of control variables that optimizes the objective function.

[0092] Solving for the optimal sequence of control variables that optimizes the objective function involves finding, through mathematical optimization methods, a series of control variables that, under predefined constraints, optimize (minimize or maximize) the objective function (e.g., energy consumption, temperature control accuracy, safety, etc.) within the prediction time domain. This ensures that the system achieves optimal performance during operation, such as minimizing fuel or electricity consumption. For example, convex optimization algorithms (such as interior-point methods and sequential quadratic programming) can be used to solve linear or quadratic programming problems; for nonlinear or nonconvex problems, heuristic algorithms such as genetic algorithms and particle swarm optimization, or nonlinear programming solvers, can be used.

[0093] Furthermore, in optimizing the objective function, different priority weights can be assigned to the temperature control requirements of different cargo holds based on the characteristics of the cargo they carry. Specifically, cargo holds with a freezing point closest to the current temperature, or those carrying cargo with higher thermal sensitivity levels, are assigned higher priority weights.

[0094] In intelligent heating control systems, the objective function is the core of the system's decision-making process, comprehensively considering multiple factors to achieve the best control effect. Assigning different priority weights to the temperature control requirements of different cargo holds means that when calculating the overall optimization objective, the temperature control requirements of all cargo holds are no longer treated equally, but rather assigned different levels of importance in the optimization process based on the inherent attributes of the cargo loaded in each hold.

[0095] These cargo characteristics may include, but are not limited to, the cargo's freezing point, boiling point, flash point, viscosity, heat sensitivity level, corrosivity, and toxicity. The allocation of priority weights can be implemented using various mathematical models. For example, it can be based on a pre-defined cargo risk level table for lookup and assignment, or a function can be used to map cargo characteristic parameters to a continuous weight value, or a fuzzy logic system can be used to comprehensively judge and allocate weights based on multiple cargo characteristic factors. This technical feature further clarifies the specific strategy for priority weight allocation. For the cargo hold with the freezing point closest to the current temperature, it means that the cargo in that hold faces the highest risk of freezing, which would cause serious economic losses and operational difficulties. Therefore, the system needs to prioritize ensuring that its temperature does not fall below the freezing point.

[0096] In practice, this can be achieved by calculating the absolute temperature difference between the current cargo temperature and its freezing point. The smaller the temperature difference, the higher the priority weight assigned. For example, an inverse proportional function or exponential function can be set to convert the temperature difference into weights. For cargo holds with higher thermal sensitivity levels, the cargo within is more sensitive to temperature changes; excessively high or low temperatures may cause the cargo to deteriorate, decompose, or become hazardous. The thermal sensitivity level can be a predefined discrete level (e.g., 1-5) or a continuous index. The higher the level, the higher the cargo's sensitivity to temperature, and the more attention and control precision the system should give it, thus assigning a higher priority weight. This can be achieved by directly mapping the thermal sensitivity level to weight values ​​or by combining it with other factors for weighted calculation.

[0097] Step 1124: Send the first control quantity in the control quantity sequence, namely the outlet temperature setpoint of the main heater and the frequency setpoint of the main circulation pump, to the actuator.

[0098] Sending the first control variable in the control sequence—the setpoint for the main heater's outlet temperature and the setpoint for the main circulating pump's frequency—to the actuator means sending the first action that needs to be executed immediately within the current control cycle from the optimized series of future control actions to the actual regulating equipment. The actuator is the device that actually regulates the main heater and the main circulating pump, such as the temperature controller of the main heater and the frequency converter of the main circulating pump. This allows the optimization results to be translated into actual physical actions, driving the system towards the target state. For example, the setpoints can be sent to the temperature controller of the main heater and the frequency converter of the main circulating pump via industrial communication protocols (such as Modbus TCP / IP, PROFIBUS); or, the central controller can directly control the actuator through analog outputs (such as 4-20mA signals) or digital outputs (such as PWM signals).

[0099] The prediction time domain ranges from 30 minutes to 2 hours, and the control cycle ranges from 1 minute to 5 minutes. These parameters are set to balance prediction accuracy, computational burden, and system response speed. The prediction time domain is the length of time the model predictive control algorithm predicts the system behavior in advance, while the control cycle is the time interval between each optimization and control action. For example, the prediction time domain can be set to 1 hour and the control cycle to 2 minutes, depending on the thermal inertia of the ship's heating system, available computational resources, and required control accuracy. Alternatively, the prediction time domain and control cycle can be dynamically adjusted based on the characteristics of the cargo in the hold and the rate of change of the external environment, for example, shortening the control cycle when the external environment changes rapidly.

[0100] The present invention utilizes a rolling execution mechanism of model predictive control algorithms, enabling the central controller to continuously and periodically reassess and optimize the system state. Within each control cycle, the system first acquires the latest system state data and ambient temperature prediction sequence, providing the most accurate real-time information for subsequent prediction and optimization, effectively avoiding inaccurate predictions and control lags caused by outdated data. Subsequently, based on an accurate system thermodynamic model, the controller can predict the future state evolution of the system under different control inputs, thereby achieving scientific prediction of system behavior and reducing the risk of blind control. By solving for the sequence of control inputs that optimizes the objective function, the system can find the control strategy with the lowest energy consumption or best performance over a future period. Finally, only the first control input in this sequence is sent to the actuator, ensuring the system can quickly respond to current demands while retaining the flexibility to adjust based on the latest information in the next control cycle. The reasonable setting of the prediction time domain and control cycle ensures long-term prediction depth while balancing the frequency and computational burden of short-term control, making the entire control process both forward-looking and real-time. This rolling optimization mechanism, combined with the aforementioned global heat production optimization steps, enables the main heater's outlet temperature setpoint and the main circulation pump's frequency setpoint to continuously and dynamically adapt to the ever-changing external environment and internal demands, thereby ensuring continuous optimization and efficient operation of heat source production.

[0101] Figure 5 A structural schematic diagram of an embodiment of the multi-compartment heated chemical tanker of the present invention is shown. The multi-compartment heated chemical tanker, such as... Figure 5 As shown, the multi-compartment heated chemical tanker 500 includes: hull 510, heating coils 520 and intelligent heating control system 530.

[0102] The hull 510 includes multiple independently spaced chemical cargo tanks 511, which are used to contain chemicals, and each tank is independent of the others. Heat coils 520 are coiled inside or on the walls of the chemical cargo tanks 510 to transport heat transfer fluid from a heat source.

[0103] The heating control system 530 is located inside the ship's hull and controls the opening of the heating coil 520. The heating control system 530 includes a central controller 531 and multiple compartment control units 532.

[0104] The central controller 531 is configured to perform a global heat production optimization step to set the outlet temperature of the main heater and the operating parameters of the main circulation pump; the central controller 531 is further configured to perform a multi-compartment dynamic heat distribution optimization step, which includes: Based on the temperature sensor network data of each cargo compartment, a dynamic demand index reflecting the real-time heat demand of each cargo compartment is calculated in real time. Based on the dynamic demand index, coordinated control commands are generated for the electric regulating valves corresponding to each cargo compartment to dynamically adjust the flow rate of heat medium to the heating coils of each cargo compartment.

[0105] Multiple compartment control units 532, each compartment control unit 532 corresponding to a cargo compartment, and including a temperature sensor network for monitoring the internal temperature of the cargo compartment, and an electrically operated regulating valve located on the inlet branch of the cargo compartment heating coil.

[0106] The core innovation of this embodiment lies in the organic unification of global heat production optimization and multi-compartment dynamic heat distribution optimization by combining the central controller 531 and the compartment control unit 532 in a hierarchical collaborative architecture. Specifically, the central controller 531 first sets the main heater outlet temperature and main circulation pump operating parameters based on the comprehensive heat demand of all cargo compartments to avoid excessive heat source operation. At the same time, each compartment control unit 532 collects the internal temperature data of the cargo compartment in real time through a temperature sensor network, providing an accurate basis for calculating the dynamic demand index. Based on this, the central controller 531 quantifies the urgency of the immediate heat demand of each cargo compartment through the dynamic demand index and generates collaborative control commands for the electric regulating valves accordingly, realizing the dynamic allocation of heat medium flow on demand.

[0107] During actual operation, the temperature sensor network continuously monitors the internal temperature of each cargo hold and transmits the data to the central controller 531. When calculating the dynamic demand index based on the temperature data, the central controller 531 comprehensively considers factors such as the deviation between the current cargo hold temperature and the target temperature, the thermal characteristics of the cargo, and environmental heat dissipation conditions. For example, when the cargo hold temperature is significantly lower than the target value and the cargo's freezing point is high, the dynamic demand index is given a higher weight; when the cargo hold temperature approaches or exceeds the target value, the index decreases accordingly or even becomes negative. Based on this index, the central controller 531 generates coordinated control commands for the electrically controlled regulating valves: cargo holds with higher dynamic demand indices are opened more fully to increase the flow of the heat transfer medium, while cargo holds with lower indices are opened less fully to suppress overheating. This dynamic adjustment mechanism effectively solves the problem of heat distribution imbalance between near-end and far-end holds.

[0108] This invention provides a computer-readable storage medium storing at least one executable instruction that, when executed on an intelligent heating control system for a multi-compartment chemical tanker cargo hold, causes the intelligent heating control system to perform an intelligent heating control method.

[0109] When the intelligent heating control system loads and executes these instructions, it can automatically sense the thermal state of each cargo compartment, predict heat demand, and collaboratively optimize heat source production and multi-branch heat distribution according to preset algorithms and strategies. This mechanism enables the intelligent heating control method to be deployed and executed in a standardized and automated manner, thereby efficiently and accurately translating the core technical means proposed in the method, such as the global heat production optimization steps and the multi-compartment dynamic heat distribution optimization steps, into actual control actions. For example, it can collaboratively control the opening of the electric regulating valve according to the dynamic demand index, and set the outlet temperature of the main heater and the operating parameters of the main circulation pump through model predictive control algorithms. In this way, the solution of this invention provides a stable and reliable operating environment for the intelligent heating control method, enabling it to fully leverage its advantages in precise temperature control, heat distribution, and energy consumption optimization.

[0110] In one specific implementation, the computer-readable storage medium can be a solid-state drive (SSD) installed in an onboard industrial control computer. The at least one executable instruction can be a binary program file written and compiled in C++, stored on the SSD. The intelligent heating control system for multi-compartment chemical tanker cargo holds can be an industrial control computer running a Linux embedded operating system. This computer is connected to multiple programmable logic controllers (PLCs) via Ethernet, with each PLC controlling the heating equipment in one or more cargo holds. When the industrial control computer starts up, its operating system loads and runs the binary program file. During runtime, the program file, according to the logic of the intelligent heating control method in this embodiment, sends control commands to the corresponding PLCs via a network interface. These commands may include adjusting the opening degree of an electric regulating valve or the temperature setpoint of the main heater, thereby enabling the entire intelligent heating control system to automatically and efficiently perform the cargo hold heating control operations.

[0111] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments of this invention are not directed to any particular programming language.

[0112] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. Similarly, for the sake of brevity and to aid in understanding one or more aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0113] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0114] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A smart heating control method for cargo holds of multi-compartment chemical tankers, characterized in that, The method includes: The global heat production optimization steps include: setting the outlet temperature of the main heater and the operating parameters of the main circulation pump based on the overall heat demand of all cargo holds; and The multi-compartment dynamic heat distribution optimization steps include: Real-time acquisition of temperature data inside each cargo compartment; Based on the temperature data of each cargo hold, a dynamic demand index reflecting the immediate heat demand of each cargo hold is calculated in real time. Based on the dynamic demand index, the opening of the electric regulating valves located on the inlet branches of the heating coils in each cargo hold is controlled in a coordinated manner to dynamically adjust the flow rate of the heat medium to each cargo hold heating coil.

2. The intelligent heating control method according to claim 2, characterized in that, The method also includes a local adaptive fine-tuning step: For a single cargo hold, the internal temperature uniformity is determined based on temperature data at different locations along the vertical direction inside the hold. If the temperature difference between the area upstream of the coil and the area downstream of the coil in the cargo hold is greater than a preset value, the supply of heat medium to the cargo hold will be reduced, provided that the temperature of the area upstream of the coil is safe.

3. The intelligent heating control method according to claim 1, characterized in that, The dynamic demand index D i The calculation is based at least on a first factor and a second factor, where the first factor is the difference between the average temperature of the cargo in the cargo hold and the preset target temperature, and the second factor is the trend of the cargo temperature in the cargo hold, dT / dt. i .

4. The intelligent heating control method according to claim 3, characterized in that, The dynamic demand index D i The calculation is further based on a third factor, which is a safety penalty term; when the temperature of the cargo in the cargo hold approaches a preset safety threshold, the safety penalty term exerts an inhibitory effect on the dynamic demand index.

5. The intelligent heating control method according to claim 4, characterized in that, The preset safety thresholds include a high-temperature safety threshold and a low-temperature safety threshold; When the temperature of the cargo inside the cargo hold approaches or exceeds the high-temperature safety threshold, the safety penalty item reduces the dynamic demand index of the cargo hold to trigger a reduction or cut-off of the heat transfer medium supply to the cargo hold. When the temperature of the cargo inside the cargo hold approaches or falls below the aforementioned low-temperature safety threshold, the safety penalty increases the dynamic demand index of the cargo hold to trigger an increase in the supply of heat medium to the cargo hold.

6. The intelligent heating control method according to claim 1, characterized in that, The coordinated control of the opening degree of the electric regulating valves located on the inlet branches of the heating coils in each cargo hold, based on the dynamic demand index, specifically includes: The dynamic demand indices calculated for all cargo holds are normalized to obtain the flow allocation weights for each cargo hold. Based on the flow distribution weights, control commands are generated for the opening of the electric regulating valves in each cargo hold, wherein cargo holds with higher dynamic demand indices receive larger valve openings, and cargo holds with lower dynamic demand indices receive smaller valve openings.

7. The intelligent heating control method according to claim 1, characterized in that, The process of setting the outlet temperature of the main heater and the operating parameters of the main circulation pump based on the overall heat demand of all cargo holds specifically includes: Obtain the total heat demand of all cargo holds, the efficiency curve of the main heater, and the ambient temperature parameters. The optimal outlet temperature setpoint of the main heater and the frequency setpoint of the main circulation pump for the future preset time period are obtained by using a model predictive control algorithm.

8. The intelligent heating control method according to claim 7, characterized in that, The model predictive control algorithm is executed in a preset predictive time domain rolling manner; in each control cycle, the following steps are performed: Obtain the current system status and the predicted environmental temperature sequence within the predicted time domain; Based on the system thermodynamic model, the evolution of the future system state under different control inputs is predicted; Find the sequence of control variables that optimizes the objective function; The first control quantity in the control quantity sequence, namely the outlet temperature setpoint of the main heater and the frequency setpoint of the main circulation pump, is sent to the actuator. The prediction time domain ranges from 30 minutes to 2 hours, and the control period ranges from 1 minute to 5 minutes.

9. A chemical tanker capable of multi-compartment heating, characterized in that, include: The hull includes multiple individually compartmented chemical cargo holds; A heating coil is coiled inside or on the wall of the chemical cargo tank for transporting a heat transfer fluid from a heat source. and A heating control system, installed within the hull, controls the opening degree of the heating coils. The heating control system includes: The central controller is configured to execute global heat production optimization steps to set the outlet temperature of the main heater and the operating parameters of the main circulation pump; Multiple compartment control units, each compartment control unit corresponding to a cargo compartment, and including a temperature sensor network for monitoring the internal temperature of the cargo compartment, and an electric regulating valve located on the inlet branch of the heating coil of the cargo compartment; The central controller is further configured to perform a multi-compartment dynamic heat distribution optimization step, which includes: Based on the temperature sensor network data of each cargo compartment, a dynamic demand index reflecting the real-time heat demand of each cargo compartment is calculated in real time. Based on the dynamic demand index, coordinated control commands are generated for the electric regulating valves corresponding to each cargo compartment to dynamically adjust the flow rate of heat medium to the heating coils of each cargo compartment.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on an intelligent heating control system for a multi-compartment chemical tanker cargo hold, causes the intelligent heating control system to perform the operation of the intelligent heating control method as described in any one of claims 1-8.

Images