Accurate heat supply energy-saving drying system and method based on kelp moisture migration monitoring

Abstract

The invention discloses a precise heat supply energy-saving drying system and method based on kelp moisture migration monitoring, and relates to the technical field of kelp drying. Based on the water content of the kelp in each drying area, the set water content of the dried kelp and the set drying time, the hot air temperature interval and the moisture removal rate interval of each drying area are solved; calculating the power demand of each drying area for a heat source; the intersection of the power requirements of all the drying areas for the heat source serves as a power regulation and control interval of the heat source; and the optimal heat source power and the optimal moisture removal rate of each area are determined. Through real-time monitoring and dynamic evaluation of the water migration rate and the water content state of the kelp in each area, regional personalized regulation and control of heat supply power and moisture removal rate are realized, the water content uniformity of the whole batch of dried kelp is greatly improved, the reconstitution property difference is remarkably reduced, and the stable quality and the high-quality rate of a final product are guaranteed.

Description

Technical Field

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

[0002] During the large-scale drying process of kelp, hot air is usually transported to various areas inside the drying equipment through a fixed heating pipeline network. Due to the differences in distance and pipeline layout characteristics between different drying areas and the heat source, different degrees of frictional losses occur during the transportation of hot air, resulting in uneven hot air temperature and air volume reaching each area. At the same time, affected by the harvesting season, stacking method, initial treatment, etc., there are significant regional or individual differences in the initial water content of the kelp material when it is fed. The combined effect of these two factors directly leads to a highly inconsistent moisture migration rate of kelp in each area during the drying process, and finally results in large fluctuations in quality indicators such as the water content and rehydration of the dried product, making it difficult to achieve a uniform drying effect as a whole, and affecting the product quality and market value.

[0003] The existing kelp drying technologies mainly focus on controlling the overall heat source power or adjusting the parameter settings of air intake and exhaust, lacking real-time perception of the key control variable - the state of the moisture migration rate inside the kelp. For the few attempts at zoning control, they also fail to couple and model the real-time moisture migration state with spatial heat loss characteristics, initial moisture differences of the material, etc., and cannot accurately quantify the differential heat energy supply and moisture exhaust requirements for different areas. Their control strategies are often highly prescriptive and weakly adaptable, and it is 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 real-time perceive the moisture migration state of kelp and accordingly perform precise and differential heat energy distribution and moisture exhaust control to solve the above problems. Summary of the Invention

[0004] To solve the above technical problems, a precise heating energy-saving drying system and method based on the monitoring of kelp moisture migration are provided. This technical solution solves the problems in the prior art that the quality indicators such as the water content and rehydration of the dried product fluctuate greatly, it is difficult to achieve a uniform drying effect as a whole, and it affects the product quality and market value.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A precise heating energy-saving drying method based on the monitoring of kelp moisture migration, comprising:

[0007] Based on the set initial heat source power and initial moisture exhaust rate, obtain the moisture migration rate of the kelp in each drying area, and substitute it into the preset water content evaluation model to evaluate the water content of the kelp in each drying area;

[0008] Based on the moisture content of kelp in each drying area, the set moisture content of dried kelp, and the set drying time, solve the hot air temperature range and moisture exhaust rate range for each drying area;

[0009] Based on the hot air temperature range of each drying area and the layout of the heat supply pipelines between each drying area and the heat source, calculate the power demand of each drying area for the heat source;

[0010] Based on the intersection of the power demands of all drying areas for the heat source, use it as the power regulation range of the heat source;

[0011] Taking the power regulation range of the heat source and the moisture exhaust rate range of each drying area as limiting conditions, and the uniformity of the final drying degree of each drying area as the goal, determine the optimal heat source power and the optimal moisture exhaust rate for each area.

[0012] Preferably, the acquisition method of the moisture content evaluation model is as follows:

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

[0014] Based on the historical drying process of kelp, determine the initial moisture content range of kelp, and obtain several sample moisture content data at gradients within the initial moisture content range of kelp;

[0015] Obtain kelp with corresponding moisture content based on the sample moisture content data, and collect its corresponding kelp moisture migration rate under the initial heat source power and the initial moisture exhaust rate as sample data;

[0016] Construct a moisture content evaluation model based on the sample data. The moisture content evaluation model takes the kelp moisture migration rate under the initial heat source power and the initial moisture exhaust rate as input and the kelp moisture content as output.

[0017] Preferably, the specific steps of solving the hot air temperature range and moisture exhaust rate range for each drying area based on the moisture content of kelp in each drying area, the set moisture content of dried kelp, and the set drying time include:

[0018] Based on the historical operation logs of the kelp drying equipment, construct a drying process model with different kelp moisture content gradients. The drying process model is used to reflect the correlation between the drying temperature, moisture exhaust rate and the kelp drying rate;

[0019] Calculate the drying rate range of the drying area based on the moisture content of kelp in each drying area, the set moisture content of dried kelp, and the set drying time;

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

[0021] Preferably, calculating 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 heat supply pipelines between each drying area and the heat source specifically includes:

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

[0023] Combined with the heat loss coefficient from the heat source to the drying area and the hot air temperature range of the drying area, determine the required temperature range of the drying area for the heat source;

[0024] Based on the required temperature range of the drying area for the heat source and the heat conversion efficiency of the personnel, determine the power demand of each drying area for the heat source.

[0025] Preferably, taking the power regulation range of the heat source and the moisture removal rate range of each drying area as the limiting conditions, and the uniformity of the final drying degree of each drying area as the goal, determining the optimal heat source power and the optimal moisture removal rate of each area specifically includes:

[0026] Based on the historical drying process experience of kelp, construct a correlation evaluation formula between the rehydration of kelp drying and the drying rate of kelp;

[0027] Use the exhaustive method to determine all feasible drying parameter groups within the power regulation range of the heat source and the moisture removal rate range of each drying area;

[0028] Based on the drying process model, determine the drying rate of each drying area corresponding to each drying parameter group respectively;

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

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

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

[0032] Real-time monitor the moisture migration rate of kelp in each drying area, dynamically evaluate the real-time moisture content of kelp in each drying area, and dynamically regulate the optimal heat source power and the optimal moisture removal rate of each area based on the real-time moisture content of kelp in the drying area.

[0033] Preferably, dynamically evaluating the real-time moisture content of kelp in each drying area and dynamically regulating the optimal heat source power and the optimal moisture exhaust rate in each area specifically includes:

[0034] Based on the historical kelp drying process, construct a moisture content evaluation model for the entire process of kelp drying. Based on the moisture content evaluation model for the entire process of kelp drying and combining with the monitoring data of kelp moisture migration during the kelp drying process, evaluate the real-time moisture content of kelp in each area in real time;

[0035] Based on the real-time moisture content of kelp in each area, re-analyze the power regulation range of the heat source and the moisture exhaust rate range of each drying area, and use the re-determined power regulation range of the heat source and the moisture exhaust rate range of each drying area as limiting conditions, and the uniformity of the final drying degree of each drying area as the goal to determine the optimal heat source power and the optimal moisture exhaust rate in each 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, the interior of which is divided into multiple drying areas with independent moisture exhaust control capabilities;

[0038] The heat source device, which has an adjustable power output module for providing hot air to the main body of the drying device;

[0039] The heating pipeline network, which connects the heat source device and each drying area in the main body of the drying device, including a main air duct and branch air ducts for transporting hot air;

[0040] The moisture monitoring unit, which is deployed in each drying area and includes at least one group of sensors for real-time detecting the moisture migration rate of kelp in this area;

[0041] The central control system, which is communicatively connected to the heat source device, each moisture exhaust 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 precise heating and energy-saving drying method based on kelp moisture migration monitoring as described above.

[0043] Optionally, the following models and databases are pre-stored or can be called in the memory of the central control system:

[0044] The moisture content evaluation model trained based on the sample data set, which takes the moisture migration rate detected under the initial set heat source power and initial moisture exhaust rate as the input and the kelp moisture content as the output;

[0045] A drying process model library, including models reflecting the correlation between drying temperature, moisture exhaust rate and kelp drying rate under different moisture content gradients of kelp;

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

[0047] A rehydration correlation evaluation formula library;

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

[0049] Evaluate the real-time moisture content of each area based on the received moisture migration rate data and the moisture content evaluation model;

[0050] Calculate the required hot air temperature range and moisture exhaust rate range of each area according to the real-time moisture content, the target setting value and the drying process model;

[0051] Deduce the power demand for the heat source of each area in combination with the heat loss coefficient database;

[0052] Calculate the intersection of the power demands of all areas as the heat source power regulation range;

[0053] Taking rehydration and moisture content as the evaluation basis, determine the globally optimal heat source power and the optimal moisture exhaust rate of each area under the limitation of the power regulation range and the moisture exhaust rate range of each area.

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

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

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

[0057] Based on the re-evaluated moisture content value, re-trigger the solution of the optimal heat source power and the optimal moisture exhaust rate of each area.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] The present invention proposes to accurately calculate the differentiated heat energy and moisture removal requirements of each partition by real-time monitoring and dynamically evaluating the moisture migration rate and moisture content status of kelp in each area, and combining the spatial heat loss characteristics of the hot air duct layout in each area, realizing regional personalized regulation of the heating power and moisture removal rate. This directly solves the core problems of uneven regional drying rates and large fluctuations in the moisture content of the final product caused by the along-way loss of hot air and the difference in the initial moisture of the materials in traditional drying, greatly improving the moisture uniformity of the whole batch of dried kelp, significantly reducing the difference in rehydration, and ensuring the stable quality and high-quality rate of the final product.

[0060] Based on the accurate analysis of the power requirements of each partition, the present invention determines a reasonable regulation range for the total heat source by obtaining the intersection of their power requirements, and then, with the goal of optimizing the drying uniformity of the whole line, globally screens the optimal heat source power value and the optimal moisture removal rate for each area, achieving a high degree of matching between the total heat supply and the actual requirements of each partition; avoiding the waste caused by the "local overheating / insufficiency" of hot air in the traditional method resulting in high total power operation or repeated adjustment, and significantly improving the utilization efficiency of the heat source power. Under the same drying target, it can effectively reduce the overall heat source energy consumption; at the same time, this optimization algorithm combined with the model preset strategy greatly improves the system decision-making efficiency and significantly shortens the regulation response time under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Flowchart of the accurate heating and energy-saving drying method based on kelp moisture migration monitoring proposed in Embodiment 1;

[0062] Figure 2 Flowchart of the acquisition method of the moisture content evaluation model proposed in Embodiment 1;

[0063] Figure 3 Flowchart of the method for solving the hot air temperature range and moisture removal rate range of each drying area proposed in Embodiment 1;

[0064] Figure 4 Flowchart of the method for calculating the power requirements of each drying area for the heat source proposed in Embodiment 1;

[0065] Figure 5 Flowchart of the method for determining the optimal heat source power and the optimal moisture removal rate for each area proposed in Embodiment 1;

[0066] Figure 6 Flowchart of the method for dynamically regulating the optimal heat source power and the optimal moisture removal rate for each area proposed in Embodiment 2. DETAILED DESCRIPTION OF THE INVENTION

[0067] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0068] Example 1. Referring to Figure 1 As shown, a precise heating 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, obtain the kelp moisture migration rate in each drying area, and substitute it into the preset moisture content evaluation model to evaluate the moisture content of kelp in each drying area;

[0070] Through the fusion application of real-time feedback of the moisture migration rate under the initial parameters and the moisture content evaluation model, the precise quantification of the actual moisture state of kelp in spatial partitions is achieved. This step solves the lag problem caused by traditional drying relying only on static moisture monitoring or fixed process parameters, provides dynamic benchmark data for subsequent differential regulation, and effectively avoids the risk of energy efficiency waste or over-drying in local areas;

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

[0072] Combining the dynamic moisture content data with the process target for modeling, calculate the temperature-moisture removal parameter range required for the current drying stage of each area. While retaining the process flexibility, this significantly improves the scientific adaptability of the partition control parameters: it not only avoids the deterioration of material quality caused by blindly high-temperature and fast moisture removal, such as the decrease in the rehydration ability of kelp, but also prevents the formation of damp heat accumulation in local areas due to insufficient moisture removal, which affects the overall efficiency. The refined definition of partition parameters fundamentally drives the implementation of precise regulation;

[0073] Based on the hot air temperature range of each drying area and the layout of the heating pipelines between each drying area and the heat source, calculate the power demand of each drying area for the heat source;

[0074] Introduce the space heat loss coefficient determined by the pipeline layout, and reverse-derive the regional hot air temperature demand into the actual power demand at the heat source end, so that the heat source output is no longer "blindly overloaded", but accurately responds to the actual needs of the outermost area, reducing ineffective heat redundancy;

[0075] Based on the intersection of the power demands of all drying areas for the heat source, as the power regulation range of the heat source;

[0076] Through the power demand intersection screening mechanism, the distributed regional demands are converged into a globally feasible solution space. This core strategy ensures the basic demands of all regions, without any region being affected by insufficient heat supply and thus affecting process stability. It strictly limits the maximum and minimum boundaries of the heat source operation, realizing a "guaranteed and non-redundant" heat energy supply framework;

[0077] Taking the power regulation range of the heat source and the moisture exhaust rate range of each drying area as the limiting conditions, and aiming at the uniformity of the final drying degree of each drying area, the optimal heat source power and the optimal moisture exhaust rate of each area are determined.

[0078] Under the constraint boundary, a multi-objective optimization algorithm is adopted, with the overall line drying uniformity as the core optimization objective to drive parameter optimization. This strategy synchronously coordinates the total heat source power and the zoned moisture exhaust actions, achieving a dynamic balance between energy input and moisture discharge.

[0079] Refer to Figure 2 As shown, the acquisition method of the water content evaluation model is as follows:

[0080] Based on the historical drying process of kelp, the average values of the optimal heat source power and the optimal moisture exhaust rate determined in the initial stage of the historical drying process are used as the initial heat source power and the initial moisture exhaust rate;

[0081] Based on the historical drying process of kelp, the initial water content range of kelp is determined, and several sample water content data are obtained by gradient in the initial water content range of kelp;

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

[0083] Based on the sample data, a water content evaluation model is constructed. The water content evaluation model takes the water migration rate of kelp at the initial heat source power and the initial moisture exhaust rate as the input and the water content of kelp as the output.

[0084] By deeply integrating historical experience with experimental data, the technical applicability and engineering efficiency have been significantly improved on the basis of ensuring accurate evaluation capabilities. This method uses the average value of the initial parameters determined in the historical optimal drying process as the unified test benchmark, systematically collects the moisture migration rates of gradient samples covering the entire initial moisture content range of kelp under the benchmark conditions, and constructs a prediction model that can accurately output the moisture content of kelp with only a single dynamic parameter of real-time moisture migration rate, 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 the actual production process with scientifically calibrated experimental data, endowing the model with strong generalization and adaptation capabilities, enabling it to effectively cope with the initial moisture differences of different batches of kelp materials, and avoiding control inaccuracies caused by fluctuations in material states. The finally achieved engineering values include significantly shortening the equipment commissioning cycle, comprehensively improving the stability and anti-interference capabilities of the system during long-term continuous operation, completely eliminating the operation and maintenance burden of repeated on-site calibration, and providing a solid and reliable intelligent decision-making basis for subsequent precise heating and zonal regulation.

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

[0086] Based on the historical operation logs of the kelp drying equipment, construct a drying process model with different kelp moisture content gradients, and the drying process model is used to reflect the correlation between the drying temperature, the moisture discharge rate and the kelp drying rate;

[0087] Calculate the drying rate range of the drying area based on the moisture content of kelp in each drying area, the set moisture content of the dried kelp, and the set drying time;

[0088] Substitute the drying rate range of the drying area into the drying process model to obtain the hot air temperature range and the moisture discharge rate range of the drying area.

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

[0090]

[0091] Wherein, is the kelp drying rate, is the drying temperature, is the moisture discharge rate, is the model coefficient, is the error constant term, n and m are linearity indexes used to control the linearity between the kelp drying rate and the drying temperature and / or the moisture discharge rate, and the value ranges of n and m are 0 - 3.

[0092] By introducing a drying process model, the system can accurately characterize the dynamic coupling mechanism between the two major control parameters of temperature and moisture removal rate and the kelp drying rate. Compared with the traditional linear model, by adjusting the values of the key exponential parameters in the model, it can flexibly adapt to the non-linear process characteristics of different drying stages. For example, in the initial high-moisture period, the weight of the moisture removal rate influencing factor can be strengthened, or in the critical dehydration stage, the exponential increase effect of temperature on the drying rate can be accurately described.

[0093] Refer to Figure 4 As shown, based on the hot air temperature range of each drying area and the layout of the heat supply pipelines between each drying area and the heat source, the specific power requirements of each drying area for the heat source are calculated as follows:

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

[0095] Combining the heat loss coefficient from the heat source to the drying area and the hot air temperature range of the drying area, determine the required temperature range of the drying area for the heat source;

[0096] Based on the required temperature range of the drying area for the heat source and the heat conversion efficiency of the personnel, determine the power requirements of each drying area for the heat source.

[0097] Deeply integrate the spatial heat loss characteristics with the accurate temperature control requirements of each zone, and achieve reverse accurate traceability from the process parameters at the drying end to the power requirements at the heat source end. By establishing a quantitative correlation model between the heat supply pipeline layout and the heat loss coefficient, effectively capture the dynamic heat attenuation law during the hot air transportation process, and combine the hot air temperature range required by the drying process in each area to directly calculate the source end temperature compensation value that meets the actual requirements at the end.

[0098] Refer to Figure 5 As shown, taking the power control range of the heat source and the moisture removal rate range of each drying area as the limiting conditions, and the uniformity of the final drying degree of each drying area as the goal, determine the optimal heat source power and the optimal moisture removal rate of each area, specifically including:

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

[0100] Use the exhaustive method to determine all feasible drying parameter groups within the power control range of the heat source and the moisture removal rate range of each drying area;

[0101] Based on the drying process model, determine the drying rate of each drying area corresponding to each drying parameter group respectively;

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

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

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

[0105] Normalize the standard deviation of the final drying moisture content of each drying area and the standard deviation of the rehydration of kelp, and use them as the normalized evaluation value of the drying moisture content and the normalized evaluation value of the rehydration.

[0106] Form the ideal optimal solution with the minimum values of the normalized evaluation value of the drying moisture content and the normalized evaluation value of the rehydration corresponding to all feasible drying parameter groups;

[0107] Form the ideal worst solution with the maximum values of the normalized evaluation value of the drying moisture content and the normalized evaluation value of the rehydration corresponding to all feasible drying parameter groups;

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

[0109] Calculate the TOPSIS evaluation value, and screen out the feasible drying parameter group corresponding to the maximum TOPSIS evaluation value as the optimal heat source power and the optimal moisture discharge rate of each area;

[0110] The formula for the TOPSIS evaluation value is:

[0111]

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

[0113] By integrating a multi-dimensional quality evaluation system and a global optimization decision-making mechanism, it has achieved a breakthrough in the deep coordination of energy efficiency regulation, regional balance, and quality assurance in the kelp drying process. By introducing a dual evaluation dimension of the rehydration index and the drying uniformity index, and combining the drying process model to accurately predict the impact of different parameter combinations on the final quality, it significantly surpasses the traditional single-objective optimization mode. The exhaustive method is used to traverse the complete feasible solution space of the heat source power and the partition dehumidification rate to ensure that the global optimal solution is discovered without omission. Furthermore, a quantitative decision-making model approaching the ideal solution is constructed based on the TOPSIS method. By calculating the spatial vector distances between each parameter group and the theoretical optimal solution and the worst solution, a process parameter scheme with comprehensive performance approaching perfection is scientifically selected. This decision-making mechanism fundamentally solves the problems of quality fluctuations and energy efficiency imbalance caused by empirical parameter adjustment, enabling the drying system to not only ensure a high degree of uniformity in the water content of each region but also maintain the stability of key qualities such as the rehydration of kelp. At the same time, the heat source power is automatically converged to the lowest necessary level that meets the quality requirements. It provides a scientific decision-making paradigm that can transform process objectives into computable, traceable, and reusable ones.

[0114] Example 2. On the basis of Example 1, this solution further includes: real-time monitoring of the kelp moisture migration rate in each drying area, and dynamically evaluating the real-time kelp water content in each drying area, and dynamically regulating the optimal heat source power and the optimal dehumidification rate of each area based on the real-time kelp water content in the drying area.

[0115] Refer to Figure 6 As shown, the specific steps are as follows:

[0116] Based on the historical kelp drying process, construct a water content evaluation model for the entire process of kelp drying. Based on the water content evaluation model for the entire process of kelp drying and the monitoring data of kelp moisture migration during the kelp drying process, real-time evaluate the real-time kelp water content in each area;

[0117] Based on the real-time kelp water content in each area, re-analyze the power regulation range of the heat source and the dehumidification rate range of each drying area, and use the re-determined power regulation range of the heat source and the dehumidification rate range of each drying area as constraints, and the final drying degree uniformity of each drying area as the goal to determine the optimal heat source power and the optimal dehumidification rate of each area.

[0118] On the basis of Example 1, this solution provides a more precise regulation solution for the refined regulation of kelp drying by constructing a water content evaluation model for the entire process of kelp drying. Based on the real-time evaluated water content data, re-calculate the feasible range of the heat source power and the boundary of the partition dehumidification rate, and dynamically embed the whole-process parameter optimization decision-making mechanism into the drying process, enabling the system to continuously perceive the changes in the material state and reconstruct the regulation logic in real time as if it has an autonomous nervous system.

[0119] Further, 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, the interior of which is divided into multiple drying areas with independent moisture exhaust control capabilities;

[0121] The heat source device, which has an adjustable power output module for providing hot air to the main body of the drying device;

[0122] The heating pipeline network, which connects the heat source device and each drying area in the main body of the drying device, including the main air duct and branch air ducts for transporting hot air;

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

[0124] The central control system, which is communicatively connected to the heat source device, each moisture exhaust execution unit, and the 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 Embodiment 1 and / or Embodiment 2.

[0126] The memory of the central control system pre-stores or can call the following models and databases:

[0127] The water content evaluation model trained based on the sample data set, which takes the moisture migration rate detected under the initial set heat source power and initial moisture exhaust rate as the input and the kelp water content as the output;

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

[0129] The heat loss coefficient database, which records the historical or preset heat loss coefficients of each drying area based on the heating pipeline layout;

[0130] The rehydration correlation evaluation formula library;

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

[0132] Evaluating the real-time water content of each area based on the received moisture migration rate data and the water content evaluation model;

[0133] Calculating the required hot air temperature range and moisture exhaust rate range of each area according to the real-time water content, target set value and the drying process model;

[0134] Derive the power demand of each area for the heat source in combination with the heat loss coefficient database;

[0135] Calculate the intersection of the power demands of all areas as the heat source power regulation interval;

[0136] Taking the rehydration property and water content as the evaluation basis, under the limitation of the power regulation interval and the moisture removal rate interval of each area, determine the globally optimal heat source power and the optimal moisture removal rate of each 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 regulation module. Specifically, the real-time regulation module is configured to:

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

[0139] Call the water content evaluation model to re-evaluate the current kelp water content of each area in real time;

[0140] Based on the re-evaluated water content value, re-trigger the solution of the optimal heat source power and the optimal moisture removal rate of each area.

[0141] In summary, the advantages of the present invention are as follows: By real-time monitoring and dynamically evaluating the moisture migration rate and its water content state of kelp in each area, combined with the spatial heat loss characteristics of the hot air duct layout in each area, accurately calculate the differentiated heat energy and moisture removal requirements of each partition, and realize the regional personalized regulation of the heating power and the moisture removal rate. This directly solves the core problems of uneven regional drying rates and large fluctuations in the final product water content caused by the along-way loss of hot air and the difference in the initial moisture of materials in traditional drying, greatly improves the water content uniformity of the whole batch of dried kelp, significantly reduces the difference in rehydration property, and ensures the stable quality and high quality 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 of this industry should understand that the present invention is not limited by the above-mentioned embodiments. What is described in the above-mentioned embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A precise heating energy-saving drying method based on monitoring the moisture migration of kelp, characterized in that, Including: Based on the set initial heat source power and initial moisture removal rate, obtain the moisture migration rate of kelp in each drying area, and substitute it into the preset moisture content evaluation model to evaluate the moisture content of kelp in each drying area; Based on the moisture content of kelp in each drying area, the set moisture content of dried kelp, and the set drying time, solve the hot air temperature range and moisture removal rate range of each drying area; Based on the hot air temperature range of each drying area and the layout of the heat supply pipeline between each drying area and the heat source, calculate the power demand of each drying area for the heat source; Based on the intersection of the power demands of all drying areas for the heat source, as the power regulation range of the heat source; Taking the power regulation range of the heat source and the moisture removal rate range of each drying area as limiting conditions, and the uniformity of the final drying degree of each drying area as the goal, determine the optimal heat source power and the optimal moisture removal rate of each area.

2. The precise heating energy-saving drying method based on kelp moisture migration monitoring according to claim 1, characterized in that, The acquisition method of the moisture content evaluation model is: Based on the historical drying process of kelp, take the average value of the optimal heat source power and the optimal moisture removal rate of each area determined in the initial stage of the historical drying process as the initial heat source power and initial moisture removal rate; Based on the historical drying process of kelp, determine the initial moisture content range of kelp, and obtain several sample moisture content data at gradients within the initial moisture content range of kelp; Obtain kelp with corresponding moisture content based on the sample moisture content data, and collect the corresponding moisture migration rate of kelp under the initial heat source power and initial moisture removal rate as sample data; Based on the sample data, construct a moisture content evaluation model, where the moisture content evaluation model takes the moisture migration rate of kelp under the initial heat source power and initial moisture removal rate as input and the moisture content of kelp as output.

3. The precise heating and energy-saving drying method based on kelp moisture migration monitoring according to claim 2, characterized in that, The specific process of solving the hot air temperature range and moisture removal rate range of each drying area based on the moisture content of kelp in each drying area, the set moisture content of dried kelp, and the set drying time includes: Based on the historical operation log of the kelp drying equipment, construct a drying process model with different moisture content gradients of kelp, and the drying process model is used to reflect the correlation between drying temperature, moisture removal rate and kelp drying rate; Based on the moisture content of kelp in each drying area, the set moisture content of dried kelp, and the set drying time, calculate the drying rate range of the drying area; 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.

4. The precise heating energy-saving drying method based on kelp moisture migration monitoring according to claim 3, characterized in that The specific process of calculating 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 heat supply pipeline between each drying area and the heat source includes: Based on the layout of the heat supply pipeline between the drying area and the heat source, determine the heat loss coefficient from the heat source to each drying area; Combined with the heat loss coefficient from the heat source to the drying area and the hot air temperature range of the drying area, determine the required temperature range of the drying area for the heat source; Based on the required temperature range of the drying area for the heat source and the heat conversion efficiency of personnel, determine the power demand of each drying area for the heat source.

5. The precise heating and energy-saving drying method based on kelp moisture migration monitoring according to claim 4, characterized in that Taking the power regulation range of the heat source and the moisture discharge rate range of each drying area as limiting conditions, and the uniformity of the final drying degree of each drying area as the goal, determining the optimal heat source power and the optimal moisture discharge rate of each area specifically includes: Based on the historical drying process experience of kelp, constructing a correlation evaluation formula between the rehydration of kelp drying and the drying rate of kelp; Using the exhaustive method to determine all feasible drying parameter groups within the power regulation range of the heat source and the moisture discharge rate range of each drying area; Based on the drying process model, determining the drying rate of each drying area corresponding to each drying parameter group respectively; Based on the drying rates of each drying area, determining the final drying water content of each drying area and the rehydration of kelp drying corresponding to each drying parameter group; Based on the final drying water content of each drying area and the rehydration of kelp drying corresponding to all feasible drying parameter groups, screening the optimal heat source power and the optimal moisture discharge rate of each area from the power regulation range of the heat source and the moisture discharge rate range of each drying area based on the TOPSIS method.

6. The precise heating energy-saving drying method based on kelp moisture migration monitoring according to any one of claims 1-5, characterized in that, It further includes: Real-time monitoring the moisture migration rate of kelp in each drying area, dynamically evaluating the real-time kelp water content in each drying area, and dynamically regulating the optimal heat source power and the optimal moisture discharge rate of each area based on the real-time kelp water content in the drying area.

7. The precise heating energy-saving drying method based on kelp moisture migration monitoring according to claim 6, wherein, The said dynamically evaluating the real-time kelp water content in each drying area and dynamically regulating the optimal heat source power and the optimal moisture discharge rate of each area based on the real-time kelp water content in the drying area specifically includes: Based on the historical kelp drying process, constructing a water content evaluation model for the whole process of kelp drying, and based on the water content evaluation model for the whole process of kelp drying and combining the kelp moisture migration monitoring data during the kelp drying process, real-time evaluating the real-time kelp water content of each area; Based on the real-time kelp water content of each area, re-analyzing the power regulation range of the heat source and the moisture discharge rate range of each drying area, and taking the re-determined power regulation range of the heat source and the moisture discharge rate range of each drying area as limiting conditions, and the uniformity of the final drying degree of each drying area as the goal, determining the optimal heat source power and the optimal moisture discharge rate of each area.

8. A precise heating and energy-saving drying system based on kelp moisture migration monitoring, characterized in that, Used to implement the precise heating and energy-saving drying method based on kelp moisture migration monitoring as described in any one of claims 1-5, including: The main body of the drying device, the interior of which is divided into multiple drying areas with independent moisture discharge control capabilities; The heat source device, which has an adjustable power output module for providing hot air to the main body of the drying device; The heat supply pipeline network, connecting the heat source device and each drying area in the main body of the drying device, including a main air duct and branch air ducts for transporting hot air; The moisture monitoring unit, deployed in each drying area, including at least one group of sensors for real-time detecting the moisture migration rate of kelp in this area; The central control system, communicatively connected to the heat source device, each moisture discharge 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 energy-saving drying method based on kelp moisture migration monitoring as described in any one of claims 1-5.

9. The precise heating and energy-saving drying system based on kelp moisture migration monitoring according to claim 8, wherein, The memory of the central control system pre-stores or can call the following models and databases: A water content evaluation model trained based on a sample data set, which takes the moisture migration rate detected under the initial set heat source power and the initial moisture removal rate as input and the kelp water content as output; A drying process model library, which contains models reflecting the correlation between the drying temperature, the moisture removal rate and the kelp drying rate under different kelp water content gradients; A heat loss coefficient database, which records the historical or preset heat loss coefficients of each drying area based on the layout of the heating pipeline; A rehydration correlation evaluation formula library; When the processor executes the program instructions, it is specifically configured to: Evaluate the real-time water content of each area based on the received moisture migration rate data and the water content evaluation model; Calculate the required hot air temperature range and moisture removal rate range of each area according to the real-time water content, the target set value and the drying process model; Deduce the power demand of each area for the heat source in combination with the heat loss coefficient database; Calculate the intersection of the power demands of all areas as the heat source power regulation range; Taking rehydration and water content as the evaluation basis, determine the globally optimal heat source power and the optimal moisture removal rate of each area under the limitation of the power regulation range and the moisture removal rate range of each area.

10. The precise heating and energy-saving drying system based on kelp moisture migration monitoring according to claim 8, characterized in that, It further includes a real-time regulation module, and the real-time regulation module is configured to: During the drying process, receive the real-time moisture migration rate continuously reported by the moisture monitoring unit; Call the water content evaluation model to re-evaluate the current kelp water content of each area in real time; Based on the re-evaluated water content value, re-trigger the solution of the optimal heat source power and the optimal moisture removal rate of each area.

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