Precision heating and energy-saving drying system and method based on kelp moisture migration monitoring

Abstract

The present invention discloses a precise heating and energy-saving drying system and method based on kelp moisture migration monitoring, which relates to the field of kelp drying technology, including: evaluating the kelp moisture content in each drying area; solving the hot air temperature range and dehumidification rate range of each drying area based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time; calculating the power demand of each drying area for the heat source; using the intersection of the power demand of all drying areas for the heat source as the power control range of the heat source; determining the optimal heat source power and the optimal dehumidification rate of each drying area. By real-time monitoring and dynamic evaluation of the moisture migration rate and moisture content status of the kelp in each drying area, regional personalized control of the heating power and dehumidification rate is achieved, greatly improving the moisture uniformity of the entire batch of dried kelp, significantly reducing the difference in rehydration, and ensuring the stable quality and high-quality rate of the final product.

Description

Technical Field

[0001] The present invention relates to the technical field of kelp drying, and in particular to a precise heating and energy-saving drying system and method based on kelp moisture migration monitoring. Background Art

[0002] During the large-scale drying of kelp, hot air is usually transported to various areas inside the drying equipment through a fixed heating pipe network. Due to the distance differences between different drying areas and the heat source and the characteristics of the pipeline layout, the hot air suffers varying degrees of loss along the way during transportation, resulting in uneven hot air temperature and air volume reaching each area. At the same time, the kelp material is affected by factors such as harvesting season, stacking method, and initial processing, and its moisture content at the time of feeding has significant regional or individual differences. The combined effect of these two factors directly leads to highly inconsistent moisture migration rates in kelp from different regions during the drying process, which ultimately manifests as large fluctuations in quality indicators such as moisture content and rehydration of the dried product, making it difficult to achieve an overall uniform drying effect, affecting product quality and market value.

[0003] Existing kelp drying technology mainly focuses on controlling the overall heat source power or adjusting the parameter settings of the intake and exhaust air, and lacks real-time perception of the key control variable - the state of the water migration rate inside the kelp. The few schemes that have attempted to implement zoning control have also failed to couple the real-time water migration state with the spatial heat loss characteristics, the initial moisture differences of the materials, etc., and are unable to accurately quantify the differentiated heat energy supply and dehumidification requirements required in different areas. Its control strategies are often highly preset and weakly adaptable, making it difficult to achieve the dual goals of optimizing energy supply and homogenizing drying results at the level of the entire production line. Therefore, there is an urgent need for an innovative method that can sense the water migration state of kelp in real time and conduct precise and differentiated heat energy distribution and dehumidification control accordingly to solve the above problems. Summary of the Invention

[0004] In order to solve the above technical problems, a precise heating and energy-saving drying system and method based on kelp moisture migration monitoring is provided. This technical solution solves the problem that the quality indicators of the product such as moisture content and rehydration after drying in the above-mentioned existing technology fluctuate greatly, making it difficult to achieve an overall uniform drying effect, thus affecting product quality and market value.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A precise heating and energy-saving drying method based on kelp moisture migration monitoring includes:

[0007] Based on the set initial heat source power and initial moisture removal rate, the kelp moisture migration rate in each drying area is obtained and substituted into the preset moisture content assessment model to assess the kelp moisture content in each drying area;

[0008] Based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time, the hot air temperature range and moisture removal rate range of each drying area are solved;

[0009] Calculate the power demand of each drying area for the heat source based on the hot air temperature range of each drying area and the layout of the heating pipes between each drying area and the heat source;

[0010] The intersection of the power requirements of all drying areas for the heat source is used as the power control range of the heat source;

[0011] Taking the power control range of the heat source and the dehumidification rate range of each drying area as the constraints, and the uniformity of the final drying degree of each drying area as the goal, the optimal heat source power and the optimal dehumidification rate of each drying area are determined.

[0012] Preferably, the method for obtaining the moisture content assessment model is:

[0013] Based on the historical drying process of kelp, the optimal heat source power and the average value of the optimal dehumidification rate of each drying area determined in the initial stage of the historical drying process were used as the initial heat source power and initial dehumidification rate;

[0014] Determine the initial moisture content range of the kelp based on the historical drying process of the kelp, and obtain moisture content data of several samples at the gradient of the initial moisture content range of the kelp;

[0015] Based on the sample moisture content data, the corresponding kelp moisture content is obtained, and the corresponding kelp moisture migration rate is collected as sample data under the initial heat source power and initial moisture removal rate;

[0016] A moisture content assessment model was constructed based on the sample data. The moisture content assessment model took the kelp moisture migration rate under the initial heat source power and the initial dehumidification rate as input, and the kelp moisture content as output.

[0017] Preferably, solving the hot air temperature range and the moisture removal rate range of each drying area based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time specifically includes:

[0018] Based on the historical operation logs of the kelp drying equipment, a drying process model for different kelp moisture content gradients was constructed. The drying process model was used to reflect the correlation between the drying temperature and moisture removal rate and the kelp drying rate.

[0019] Calculating a drying rate interval for each drying area based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time;

[0020] The drying rate range of the drying area is substituted into the drying process model to obtain the hot air temperature range and moisture removal rate range of the drying area.

[0021] Preferably, the calculation of the power demand of each drying area for the heat source based on the hot air temperature range of each drying area and the layout of the heating pipes between each drying area and the heat source specifically includes:

[0022] Based on the layout of the heating pipes between the drying area and the heat source, determine the heat loss coefficient from the heat source to each drying area;

[0023] Determine the required temperature range for the heat source in the drying area based on the heat loss coefficient from the heat source to the drying area and the hot air temperature range in the drying area.

[0024] The power requirement of each drying area for the heat source is determined based on the required temperature range of the drying area for the heat source and the heat conversion efficiency of the heat source.

[0025] Preferably, the power control range of the heat source and the dehumidification rate range of each drying zone are used as the limiting conditions, and the uniformity of the final drying degree of each drying zone is used as the goal. Determining the optimal heat source power and the optimal dehumidification rate of each drying zone specifically includes:

[0026] Based on the historical drying process experience of kelp, an evaluation formula for the correlation between kelp drying rehydration and kelp drying rate was constructed;

[0027] An exhaustive method is used to determine all feasible drying parameter groups within the power control range of the heat source and the dehumidification rate range of each drying area.

[0028] Based on the drying process model, the drying rate of each drying zone corresponding to each drying parameter group is determined respectively;

[0029] Determine the final drying moisture content of each drying area and the drying rehydration of kelp corresponding to each drying parameter group based on the drying rate of each drying area;

[0030] Based on the final drying moisture content of each drying area corresponding to all feasible drying parameter groups and the drying rehydration property of kelp, the optimal heat source power and the optimal dehumidification rate of each drying area were selected from the power control range of the heat source and the dehumidification rate range of each drying area based on the TOPSIS method.

[0031] Preferably, the precise heating and energy-saving drying method based on kelp moisture migration monitoring further includes:

[0032] The kelp moisture migration rate in each drying area is monitored in real time, and the real-time kelp moisture content in each drying area is dynamically evaluated. Based on the real-time kelp moisture content in the drying area, the optimal heat source power and the optimal dehumidification rate in each drying area are dynamically adjusted.

[0033] Preferably, the dynamic evaluation of the real-time kelp moisture content in each drying area and the dynamic regulation of the optimal heat source power and the optimal dehumidification rate of each drying area based on the real-time kelp moisture content in the drying area specifically include:

[0034] Based on the historical kelp drying process, a moisture content assessment model for the entire kelp drying process was constructed. This model, combined with the kelp moisture migration monitoring data during the kelp drying process, was used to assess the real-time kelp moisture content in each drying area.

[0035] Based on the real-time kelp moisture content in each drying area, the power control range of the heat source and the dehumidification rate range of each drying area are re-analyzed. The re-determined power control range of the heat source and the dehumidification rate range of each drying area are used as constraints, and the final drying degree uniformity of each drying area is taken as the goal to determine the optimal heat source power and the optimal dehumidification rate of each drying area.

[0036] Furthermore, a precise heating and energy-saving drying system based on kelp moisture migration monitoring is proposed, including:

[0037] The main body of the drying device is divided into multiple drying areas with independent moisture removal control capabilities;

[0038] A heat source device having an adjustable power output module for providing hot air to the drying device body;

[0039] A heating pipe network connects the heat source device with the drying areas in the main body of the drying device, including main air ducts and branch air ducts for conveying hot air;

[0040] A moisture monitoring unit is deployed in each drying area and includes at least one set of sensors for real-time detection of the moisture migration rate of the kelp in the drying area;

[0041] A central control system, communicatively connected to the heat source device, each dehumidification execution unit, and the moisture monitoring unit;

[0042] The central control system includes a processor and a memory; the memory stores program instructions, and when the processor executes the program instructions, the system executes the above-mentioned precise heating and energy-saving drying method based on kelp moisture migration monitoring.

[0043] Optionally, the memory of the central control system pre-stores or can call the following models and databases:

[0044] A moisture content assessment model trained based on a sample data set, wherein the moisture content assessment model uses the moisture migration rate detected under the initial set heat source power and initial dehumidification rate as input and uses the kelp moisture content as output;

[0045] The drying process model library includes models reflecting the correlation between drying temperature, moisture removal rate and kelp drying rate under different kelp moisture content gradients;

[0046] Heat loss coefficient database, recording the historical or preset heat loss coefficients of each drying area based on the layout of the heating pipes;

[0047] Rehydration correlation evaluation formula library;

[0048] When the processor executes the program instructions, it is specifically used to:

[0049] The real-time kelp moisture content in each drying area was evaluated based on the received moisture migration rate and moisture content assessment model;

[0050] Calculate the hot air temperature range and moisture removal rate range required for each drying area based on the real-time kelp moisture content, the set dried kelp moisture content, and the drying process model;

[0051] Combined with the heat loss coefficient database, the power demand of each drying area for the heat source is derived;

[0052] Calculate the intersection of the power requirements of all drying areas and the power requirements of the heat source as the power control range of the heat source;

[0053] Taking the final drying moisture content and the drying rehydration property of kelp as the evaluation basis, the global optimal heat source power and the optimal dehumidification rate of each drying area are determined under the constraints of the power control range and the dehumidification rate range of each drying area.

[0054] Optionally, the precise heating and energy-saving drying system based on kelp moisture migration monitoring further includes a real-time control module. Specifically, the real-time control module is configured as follows:

[0055] During the drying process, receiving the real-time moisture migration rate continuously reported by the moisture monitoring unit;

[0056] Call the moisture content assessment model to re-evaluate the current kelp moisture content in each drying area in real time;

[0057] Based on the re-evaluated current kelp moisture content, the optimal heat source power and the optimal moisture removal rate of each drying area are re-triggered.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] This invention proposes real-time monitoring and dynamic evaluation of the water migration rate and moisture content of kelp in each region. Combined with the spatial heat loss characteristics of each region's hot air duct layout, this method accurately calculates the differentiated heat energy and dehumidification requirements of each zone, achieving personalized regional regulation of heating power and dehumidification rate. This directly addresses the core issue of traditional drying, which results in varying regional drying rates and large fluctuations in the moisture content of the finished product due to hot air losses along the drying process and differences in the initial moisture content of the material. This significantly improves the moisture uniformity of the entire batch of dried kelp, significantly reduces differences in rehydration, and ensures the stable quality and superiority of the final product.

[0060] Based on a precise analysis of each zone's power requirements, this method determines the appropriate control range for the total heat source by intersecting their power requirements. Furthermore, with drying uniformity across the entire line as the optimization goal, it globally selects the optimal heat source power value and optimal dehumidification rate for each zone, achieving a close match between the total heat supply and the actual needs of each zone. This method avoids the wasteful operation of the total power or repeated adjustments caused by "local overheating / insufficient" hot air in traditional methods, significantly improving heat source power utilization efficiency. While maintaining the same drying target, it can effectively reduce overall heat source energy consumption. Furthermore, this optimization algorithm, combined with a model-preset strategy, significantly improves system decision-making efficiency and significantly shortens control response time under complex operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of the precise heating and energy-saving drying method based on kelp moisture migration monitoring proposed in Example 1;

[0062] Figure 2 This is a flow chart of the method for obtaining the moisture content assessment model proposed in Example 1;

[0063] Figure 3 This is a flow chart of the method for solving the hot air temperature range and moisture removal rate range of each drying area proposed in Example 1;

[0064] Figure 4 This is a flow chart of the method for calculating the power requirement of each drying area for the heat source proposed in Example 1;

[0065] Figure 5 This is a flow chart of the method for determining the optimal heat source power and the optimal dehumidification rate for each area proposed in Example 1;

[0066] Figure 6 This is a flow chart of the method for dynamically regulating the optimal heat source power and the optimal dehumidification rate of each area proposed in Example 2. DETAILED DESCRIPTION

[0067] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0068] Example 1, refer to Figure 1 As shown in the figure, the precise heating and energy-saving drying method based on kelp moisture migration monitoring includes:

[0069] Based on the set initial heat source power and initial moisture removal rate, the kelp moisture migration rate in each drying area is obtained and substituted into the preset moisture content assessment model to assess the kelp moisture content in each drying area;

[0070] By integrating real-time feedback of moisture migration rates under initial parameters with a moisture content assessment model, we achieve precise quantification of the actual moisture status of spatially zoned kelp. This step overcomes the hysteresis problem caused by traditional drying that relies solely on static moisture content monitoring or fixed process parameters, providing dynamic benchmark data for subsequent differential control, effectively avoiding energy waste or the risk of overdrying in certain areas.

[0071] Based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time, the hot air temperature range and moisture removal rate range of each drying area are solved;

[0072] Dynamic moisture content data is combined with process objectives to create a model that calculates the temperature-dehumidification parameter range required to adapt to the current drying stage in each zone. This approach significantly improves the scientific adaptability of zoning control parameters while maintaining process flexibility. It avoids material quality degradation caused by blind high-temperature rapid dehumidification, such as the decrease in kelp rehydration, and prevents the accumulation of moisture and heat in local areas due to insufficient dehumidification, which affects overall efficiency. The refined definition of zoning parameters fundamentally drives the implementation of precise control.

[0073] Calculate the power demand of each drying area for the heat source based on the hot air temperature range of each drying area and the layout of the heating pipes between each drying area and the heat source;

[0074] By introducing the spatial heat loss coefficient determined by the pipeline layout, the regional hot air temperature demand is reversely deduced into the actual power demand at the heat source end. This ensures that the heat source output no longer "blindly overloads" but accurately responds to the actual demand of the terminal area, reducing ineffective heat redundancy.

[0075] The intersection of the power requirements of all drying areas for the heat source is used as the power control range of the heat source;

[0076] Through the power demand intersection screening mechanism, distributed regional demands are converged into a globally feasible solution space. This core strategy ensures that all regional basic demands are met, preventing any regional heat supply from affecting process stability. It strictly limits the maximum and minimum operating boundaries of the heat source, thus achieving a "guaranteed and non-redundant" heat supply framework.

[0077] Taking the power control range of the heat source and the dehumidification rate range of each drying area as the constraints, and the uniformity of the final drying degree of each drying area as the goal, the optimal heat source power and the optimal dehumidification rate of each drying area are determined.

[0078] Within the constraints, a multi-objective optimization algorithm was employed, with drying uniformity across the entire line as the core optimization objective, to drive parameter optimization. This strategy simultaneously coordinated the total heat source power with the zoned dehumidification action, achieving a dynamic balance between energy input and moisture removal.

[0079] Reference Figure 2 As shown in Figure 2, the method for obtaining the moisture content assessment model is:

[0080] Based on the historical drying process of kelp, the optimal heat source power and the average value of the optimal dehumidification rate of each drying area determined in the initial stage of the historical drying process were used as the initial heat source power and initial dehumidification rate;

[0081] Determine the initial moisture content range of the kelp based on the historical drying process of the kelp, and obtain moisture content data of several samples at the gradient of the initial moisture content range of the kelp;

[0082] Based on the sample moisture content data, the corresponding kelp moisture content is obtained, and the corresponding kelp moisture migration rate is collected as sample data under the initial heat source power and initial moisture removal rate;

[0083] A moisture content assessment model was constructed based on the sample data. The moisture content assessment model took the kelp moisture migration rate under the initial heat source power and the initial dehumidification rate as input, and the kelp moisture content as output.

[0084] By deeply integrating historical experience and experimental data, the technical applicability and engineering efficiency have been greatly improved while ensuring accurate assessment capabilities. This method uses the average value of the initial parameters determined in the historical optimal drying process as a unified test benchmark, systematically collects the moisture migration rate of gradient samples covering the entire initial moisture content range of kelp under benchmark conditions, and constructs a prediction model that can accurately output the moisture content of kelp with only a single real-time moisture migration rate dynamic parameter, completely getting rid of the dependence on complex multi-source sensors, and significantly reducing the system hardware cost and deployment threshold. At the same time, this method creatively integrates the reliable experience of actual production processes with scientifically calibrated experimental data, giving the model a strong generalization and adaptability, enabling it to effectively deal with the initial moisture differences of different batches of kelp materials, and avoiding control inaccuracies caused by fluctuations in material status. The ultimate engineering value achieved includes significantly shortening the equipment commissioning cycle, comprehensively improving the stability and anti-interference ability of the system in long-term continuous operation, and completely eliminating the operation and maintenance burden of repeated on-site calibration, providing a solid and reliable intelligent decision-making foundation for subsequent precise heating and zoning control.

[0085] Reference Figure 3 As shown, based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time, solving the hot air temperature range and moisture removal rate range of each drying area specifically includes:

[0086] Based on the historical operation logs of the kelp drying equipment, a drying process model for different kelp moisture content gradients was constructed. The drying process model is used to reflect the correlation between drying temperature and moisture removal rate and kelp drying rate.

[0087] Calculating a drying rate interval for each drying area based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time;

[0088] The drying rate range of the drying area is substituted into the drying process model to obtain the hot air temperature range and moisture removal rate range of the drying area.

[0089] Specifically, in some preferred embodiments, the drying process model is set as follows:

[0090]

[0091] in, is the drying rate of kelp, is the drying temperature, is the moisture removal rate, is the model coefficient, is the error constant term, n and m are linearity indicators, which are used to control the linearity of kelp drying rate and drying temperature and / or dehumidification rate. The value range of n and m is 0-3.

[0092] By introducing a drying process model, the system accurately characterizes the dynamic coupling mechanism between the two control parameters, temperature and dehumidification rate, and the kelp drying rate. Compared to traditional linear models, this model can flexibly adapt to the nonlinear process characteristics of different drying stages by adjusting the values ​​of key exponential parameters. For example, it can strengthen the weight of the dehumidification rate factor during the initial high-moisture period, or accurately describe the exponential effect of temperature on the drying rate during the critical dehydration stage.

[0093] Reference Figure 4 As shown, based on the hot air temperature range of each drying area and the layout of the heating pipes between each drying area and the heat source, the power demand of each drying area for the heat source is calculated, including:

[0094] Based on the layout of the heating pipes between the drying area and the heat source, determine the heat loss coefficient from the heat source to each drying area;

[0095] Determine the required temperature range for the heat source in the drying area based on the heat loss coefficient from the heat source to the drying area and the hot air temperature range in the drying area.

[0096] The power requirement of each drying area for the heat source is determined based on the required temperature range of the drying area for the heat source and the heat conversion efficiency of the heat source.

[0097] By deeply integrating spatial heat loss characteristics with the precise temperature control requirements of each zone, we achieve reverse tracing from process parameters at the drying end to power requirements at the heat source. By establishing a quantitative correlation model between heating pipeline layout and heat loss coefficients, we effectively capture the dynamic thermal attenuation patterns during hot air delivery. Combined with the hot air temperature ranges required by the drying process in each zone, we directly calculate the source-end temperature compensation value that meets the actual needs of the end user.

[0098] Reference Figure 5 As shown in the figure, with the power control range of the heat source and the dehumidification rate range of each drying area as the constraints, and the uniformity of the final drying degree of each drying area as the goal, the optimal heat source power and the optimal dehumidification rate of each drying area are determined specifically including:

[0099] Based on the historical drying process experience of kelp, an evaluation formula for the correlation between kelp drying rehydration and kelp drying rate was constructed;

[0100] An exhaustive method is used to determine all feasible drying parameter groups within the power control range of the heat source and the dehumidification rate range of each drying area.

[0101] Based on the drying process model, the drying rate of each drying zone corresponding to each drying parameter group is determined respectively;

[0102] Determine the final drying moisture content of each drying area and the drying rehydration of kelp corresponding to each drying parameter group based on the drying rate of each drying area;

[0103] Based on the final drying moisture content of each drying area corresponding to all feasible drying parameter groups and the drying rehydration property of kelp, the optimal heat source power and the optimal dehumidification rate of each drying area were selected from the power control range of the heat source and the dehumidification rate range of each drying area based on the TOPSIS method.

[0104] Specifically, the specific process of the TOPSIS method is:

[0105] The standard deviation of the final drying moisture content of each drying area and the standard deviation of the drying rehydration of kelp were normalized and used as the standard evaluation value of the drying moisture content and the standard evaluation value of the rehydration;

[0106] The ideal optimal solution is formed by taking the minimum value of the normalized evaluation value of drying moisture content and the normalized evaluation value of rehydration corresponding to all feasible drying parameter groups;

[0107] The ideal worst solution is formed by taking the maximum value of the normalized evaluation value of drying moisture content and the normalized evaluation value of rehydration corresponding to all feasible drying parameter groups;

[0108] Calculate the vector distances between the normalized evaluation value of drying moisture content and the normalized evaluation value of rehydration capacity corresponding to each feasible drying parameter group and the ideal optimal solution and the ideal worst solution respectively;

[0109] Calculate the TOPSIS evaluation value and select the feasible drying parameter group corresponding to the maximum TOPSIS evaluation value as the optimal heat source power and the optimal dehumidification rate in each area;

[0110] The formula for TOPSIS evaluation value is:

[0111]

[0112] is the TOPSIS evaluation value, is the vector distance from the ideal optimal solution, is the vector distance to the ideal worst solution.

[0113] By integrating a multi-dimensional quality evaluation system with a global optimization decision-making mechanism, a breakthrough has been achieved in deeply synergizing energy efficiency control, regional balance, and quality assurance in the kelp drying process. By introducing dual evaluation dimensions—rehydration and drying uniformity—and combining them with a drying process model, the system accurately predicts the impact of different parameter combinations on end-product quality, significantly surpassing traditional single-objective optimization models. An exhaustive search method traverses the entire feasible solution space for heat source power and zoned dehumidification rate, ensuring the complete discovery of the global optimal solution. A quantitative decision-making model approaching the ideal solution is then constructed based on the TOPSIS method. By calculating the spatial vector distance between each parameter group and the theoretical optimal and worst solutions, process parameter solutions with near-perfect overall performance are scientifically selected. This decision-making mechanism fundamentally addresses the issues of quality fluctuation and energy efficiency imbalance caused by empirical parameter adjustment. This allows the drying system to ensure high uniformity of moisture content across zones while maintaining the stability of key qualities such as kelp rehydration, while automatically converging the heat source power to the minimum necessary level to meet quality requirements. This provides a scientific decision-making paradigm that transforms process objectives into calculable, traceable, and reusable ones.

[0114] Example 2. Based on Example 1, this solution also includes: real-time monitoring of the kelp moisture migration rate in each drying area, and dynamic evaluation of the real-time kelp moisture content in each drying area, and dynamic regulation of the optimal heat source power and the optimal dehumidification rate in each drying area based on the real-time kelp moisture content in the drying area.

[0115] Reference Figure 6 As shown, the specific steps are:

[0116] Based on the historical kelp drying process, a moisture content assessment model for the entire kelp drying process was constructed. This model, combined with the kelp moisture migration monitoring data during the kelp drying process, was used to assess the real-time kelp moisture content in each drying area.

[0117] Based on the real-time kelp moisture content in each drying area, the power control range of the heat source and the dehumidification rate range of each drying area are re-analyzed. The re-determined power control range of the heat source and the dehumidification rate range of each drying area are used as constraints, and the final drying degree uniformity of each drying area is taken as the goal to determine the optimal heat source power and the optimal dehumidification rate of each drying area.

[0118] Based on Example 1, this solution provides a further precise control solution for the refined control of kelp drying by constructing a moisture content assessment model for the entire kelp drying process. It recalculates the feasible range of heat source power and the boundary of partition dehumidification rate based on the real-time assessed moisture content data, and dynamically embeds the full-process parameter optimization decision-making mechanism into the drying process, so that the system can continuously perceive changes in material status and reconstruct the control logic in real time like an autonomous nervous system.

[0119] Furthermore, based on the same inventive concept as the above method, this solution also proposes a precise heating and energy-saving drying system based on kelp moisture migration monitoring, including:

[0120] The main body of the drying device is divided into multiple drying areas with independent moisture removal control capabilities;

[0121] A heat source device having an adjustable power output module for providing hot air to the drying device body;

[0122] A heating pipe network connects the heat source device with the drying areas in the main body of the drying device, including main air ducts and branch air ducts for conveying hot air;

[0123] A moisture monitoring unit is deployed in each drying area and includes at least one set of sensors for real-time detection of the moisture migration rate of the kelp in the drying area;

[0124] Central control system, communicating with heat source device, each dehumidification execution unit, and moisture monitoring unit;

[0125] The central control system includes a processor and a memory; the memory stores program instructions, and when the processor executes the program instructions, the system executes the precise heating and energy-saving drying method based on kelp moisture migration monitoring proposed in Example 1 and / or Example 2.

[0126] The following models and databases are pre-stored or can be called in the memory of the central control system:

[0127] A moisture content assessment model trained based on a sample dataset takes the moisture migration rate detected under the initial set heat source power and initial dehumidification rate as input and the kelp moisture content as output;

[0128] The drying process model library includes models reflecting the correlation between drying temperature, moisture removal rate and kelp drying rate under different kelp moisture content gradients;

[0129] Heat loss coefficient database, recording the historical or preset heat loss coefficients of each drying area based on the layout of the heating pipes;

[0130] Rehydration correlation evaluation formula library;

[0131] When the processor executes program instructions, it is specifically used to:

[0132] The real-time kelp moisture content in each drying area was evaluated based on the received moisture migration rate and moisture content assessment model;

[0133] Calculate the hot air temperature range and moisture removal rate range required for each drying area based on the real-time kelp moisture content, the set dried kelp moisture content, and the drying process model;

[0134] Combined with the heat loss coefficient database, the power demand of each drying area for the heat source is derived;

[0135] Calculate the intersection of the power requirements of all drying areas and the power requirements of the heat source as the power control range of the heat source;

[0136] Taking the final drying moisture content and the drying rehydration property of kelp as the evaluation basis, the global optimal heat source power and the optimal dehumidification rate of each drying area are determined under the constraints of the power control range and the dehumidification rate range of each drying area.

[0137] In some preferred embodiments, the above-mentioned precise heating and energy-saving drying system based on kelp moisture migration monitoring further includes a real-time control module. Specifically, the real-time control module is configured as follows:

[0138] During the drying process, receiving the real-time moisture migration rate continuously reported by the moisture monitoring unit;

[0139] Call the moisture content assessment model to re-evaluate the current kelp moisture content in each drying area in real time;

[0140] Based on the re-evaluated current kelp moisture content, the optimal heat source power and the optimal moisture removal rate of each drying area are re-triggered.

[0141] In summary, the advantages of the present invention lie in: by real-time monitoring and dynamic evaluation of the water migration rate and moisture content of kelp in each region, combined with the spatial heat loss characteristics of the hot air duct layout in each region, the differentiated heat energy and dehumidification requirements of each zone can be accurately calculated, achieving regional personalized regulation of heating power and dehumidification rate. This directly addresses the core issue of traditional drying, which results in varying regional drying rates and large fluctuations in the moisture content of the final product due to hot air losses along the drying process and differences in the initial moisture content of the material. It significantly improves the moisture uniformity of the entire batch of dried kelp, significantly reduces differences in rehydration, and ensures the stable quality and excellent rate of the final product.

[0142] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A precise heating and energy-saving drying method based on kelp moisture migration monitoring, characterized in that: include: Based on the set initial heat source power and initial moisture removal rate, the kelp moisture migration rate in each drying area is obtained and substituted into the preset moisture content assessment model to assess the kelp moisture content in each drying area; Based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time, the hot air temperature range and moisture removal rate range of each drying area are solved; Calculate the power demand of each drying area for the heat source based on the hot air temperature range of each drying area and the layout of the heating pipes between each drying area and the heat source; The intersection of the power requirements of all drying areas for the heat source is used as the power control range of the heat source; The optimal heat source power and optimal dehumidification rate for each drying zone are determined by taking the power control range of the heat source and the dehumidification rate range of each drying zone as constraints and the uniformity of the final drying degree of each drying zone as the goal. The method of calculating the hot air temperature range and the moisture removal rate range of each drying area based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time specifically includes: Based on the historical operation logs of the kelp drying equipment, a drying process model for different kelp moisture content gradients was constructed. The drying process model was used to reflect the correlation between the drying temperature and moisture removal rate and the kelp drying rate. Calculating a drying rate interval for each drying area based on the kelp moisture content of each drying area, the set moisture content of the dried kelp, and the set drying time; Substitute the drying rate range of the drying area into the drying process model to obtain the hot air temperature range and moisture removal rate range of the drying area; The method of determining the optimal heat source power and the optimal dehumidification rate of each drying area with the power control range of the heat source and the dehumidification rate range of each drying area as constraints and the uniformity of the final drying degree of each drying area as the goal specifically includes: Based on the historical drying process experience of kelp, an evaluation formula for the correlation between kelp drying rehydration and kelp drying rate was constructed; An exhaustive method is used to determine all feasible drying parameter groups within the power control range of the heat source and the dehumidification rate range of each drying area. Based on the drying process model, the drying rate of each drying zone corresponding to each drying parameter group is determined respectively; Determine the final drying moisture content of each drying area and the drying rehydration of kelp corresponding to each drying parameter group based on the drying rate of each drying area; Based on the final drying moisture content of each drying area corresponding to all feasible drying parameter groups and the drying rehydration property of kelp, the optimal heat source power and the optimal dehumidification rate of each drying area were selected from the power control range of the heat source and the dehumidification rate range of each drying area based on the TOPSIS method.

2. The precise heating and energy-saving drying method based on kelp moisture migration monitoring according to claim 1 is characterized in that: The method for obtaining the moisture content assessment model is: Based on the historical drying process of kelp, the optimal heat source power and the average value of the optimal dehumidification rate of each drying area determined in the initial stage of the historical drying process were used as the initial heat source power and initial dehumidification rate; Determine the initial moisture content range of the kelp based on the historical drying process of the kelp, and obtain moisture content data of several samples at the gradient of the initial moisture content range of the kelp; Based on the sample moisture content data, the corresponding kelp moisture content is obtained, and the corresponding kelp moisture migration rate is collected as sample data under the initial heat source power and initial moisture removal rate; A moisture content assessment model was constructed based on the sample data. The moisture content assessment model took the kelp moisture migration rate under the initial heat source power and the initial dehumidification rate as input, and the kelp moisture content as output.

3. The precise heating and energy-saving drying method based on kelp moisture migration monitoring according to claim 2 is characterized in that: The calculation of the power demand of each drying area for the heat source based on the hot air temperature range of each drying area and the layout of the heating pipes between each drying area and the heat source specifically includes: Based on the layout of the heating pipes between the drying area and the heat source, determine the heat loss coefficient from the heat source to each drying area; Determine the required temperature range for the heat source in the drying area based on the heat loss coefficient from the heat source to the drying area and the hot air temperature range in the drying area. The power requirement of each drying area for the heat source is determined based on the required temperature range of the drying area for the heat source and the heat conversion efficiency of the heat source.

4. The precise heating and energy-saving drying method based on kelp moisture migration monitoring according to any one of claims 1 to 3, characterized in that: Also includes: The kelp moisture migration rate in each drying area is monitored in real time, and the real-time kelp moisture content in each drying area is dynamically evaluated. Based on the real-time kelp moisture content in the drying area, the optimal heat source power and the optimal dehumidification rate in each drying area are dynamically adjusted.

5. The precise heating and energy-saving drying method based on kelp moisture migration monitoring according to claim 4 is characterized in that: The dynamic evaluation of the real-time kelp moisture content in each drying area and the dynamic regulation of the optimal heat source power and the optimal dehumidification rate of each drying area based on the real-time kelp moisture content in the drying area specifically include: Based on the historical kelp drying process, a moisture content assessment model for the entire kelp drying process was constructed. This model, combined with the kelp moisture migration monitoring data during the kelp drying process, was used to assess the real-time kelp moisture content in each drying area. Based on the real-time kelp moisture content in each drying area, the power control range of the heat source and the dehumidification rate range of each drying area are re-analyzed. The re-determined power control range of the heat source and the dehumidification rate range of each drying area are used as constraints, and the final drying degree uniformity of each drying area is taken as the goal to determine the optimal heat source power and the optimal dehumidification rate of each drying area.

6. A precise heating and energy-saving drying system based on kelp moisture migration monitoring is characterized by: The method for realizing the precise heating and energy-saving drying method based on kelp moisture migration monitoring as claimed in any one of claims 1 to 3 comprises: The main body of the drying device is divided into multiple drying areas with independent moisture removal control capabilities; A heat source device having an adjustable power output module for providing hot air to the drying device body; A heating pipe network connects the heat source device with the drying areas in the main body of the drying device, including main air ducts and branch air ducts for conveying hot air; A moisture monitoring unit is deployed in each drying area and includes at least one set of sensors for real-time detection of the moisture migration rate of the kelp in the drying area; A central control system, communicatively connected to the heat source device, each dehumidification execution unit, and the moisture monitoring unit; The central control system includes a processor and a memory; the memory stores program instructions, and when the processor executes the program instructions, the system executes the precise heating and energy-saving drying method based on kelp moisture migration monitoring as described in any one of claims 1-5.

7. The precise heating and energy-saving drying system based on kelp moisture migration monitoring according to claim 6 is characterized in that: The memory of the central control system pre-stores or can call the following models and databases: A moisture content assessment model trained based on a sample data set, wherein the moisture content assessment model uses the moisture migration rate detected under the initial set heat source power and initial dehumidification rate as input and uses the kelp moisture content as output; The drying process model library includes models reflecting the correlation between drying temperature, moisture removal rate and kelp drying rate under different kelp moisture content gradients; Heat loss coefficient database, recording the historical or preset heat loss coefficients of each drying area based on the layout of the heating pipes; Rehydration correlation evaluation formula library; When the processor executes the program instructions, it is specifically used to: The real-time kelp moisture content in each drying area was evaluated based on the received moisture migration rate and moisture content assessment model; Calculate the hot air temperature range and moisture removal rate range required for each drying area based on the real-time kelp moisture content, the set dried kelp moisture content, and the drying process model; Combined with the heat loss coefficient database, the power demand of each drying area for the heat source is derived; Calculate the intersection of the power requirements of all drying areas and the power requirements of the heat source as the power control range of the heat source; Taking the final drying moisture content and the drying rehydration property of kelp as the evaluation basis, the global optimal heat source power and the optimal dehumidification rate of each drying area are determined under the constraints of the power control range and the dehumidification rate range of each drying area.

8. The precise heating and energy-saving drying system based on kelp moisture migration monitoring according to claim 6 is characterized in that: It also includes a real-time control module, which is configured as follows: During the drying process, receiving the real-time moisture migration rate continuously reported by the moisture monitoring unit; Call the moisture content assessment model to re-evaluate the current kelp moisture content in each drying area in real time; Based on the re-evaluated current kelp moisture content, the optimal heat source power and the optimal moisture removal rate of each drying area are re-triggered.

Images