Sewage treatment energy consumption optimization method based on internet of things

Abstract

The application discloses a sewage treatment energy consumption optimization method based on the Internet of Things, and the method comprises the following steps: monitoring sewage treatment equipment to realize equipment optimization; planning a strategy based on the optimized sewage treatment equipment and the type and volume of sewage; treating sewage according to the sewage treatment strategy, recycling waste heat generated during treatment, improving sludge settling technology by using advanced oxidation technology, and realizing energy consumption compensation through the recycled waste heat and the improved sludge settling rate. The application can help sewage treatment plants realize the double balance of energy consumption and energy utilization efficiency by reasonably designing a treatment process, scientifically controlling aeration quantity, timely repairing and replacing aged equipment, utilizing waste heat, and adopting energy-saving technologies and new materials, so that the sewage treatment plants can contribute to environmental protection, save costs, and realize the goal of energy saving and emission reduction.

Description

Technical Field

[0001] This invention relates to the field of energy consumption optimization technology, and more specifically to an energy consumption optimization method for wastewater treatment based on the Internet of Things. Background Technology

[0002] With the continuous development of urbanization and industrialization, wastewater has become an increasingly important environmental problem. Wastewater treatment processes can effectively transform harmful substances in wastewater into safe discharge substances. However, wastewater treatment plants are energy-intensive facilities, so how to achieve energy-saving optimization and control has become one of the hot topics in wastewater treatment research both domestically and internationally.

[0003] In recent years, scholars and enterprises at home and abroad have conducted extensive research on energy-saving control in wastewater treatment plants. This mainly includes the following four aspects: (1) Improvement and optimization of wastewater treatment equipment. For example, improving the structural design of biochemical reactors to reduce reactor energy consumption; adopting more efficient aerators and flushing equipment to reduce and optimize equipment operating time, etc. (2) Developing reasonable operating strategies. Adjusting parameters such as aeration flow rate and aeration time can guide the oxygen consumption of organisms in wastewater, achieving high energy efficiency while also controlling the stability of water quality indicators and reducing energy consumption. (3) Real-time monitoring of control indicators. During wastewater treatment, real-time monitoring of different parameter indicators can better reflect the operating status of the facilities and make necessary control adjustments, thereby helping to improve the overall efficiency of the equipment and energy utilization rate. (4) Adoption of new technologies and materials. The application of new technologies and materials can not only reduce the impact on the environment but also improve the energy utilization efficiency of the equipment.

[0004] However, there was not much mention of equipment safety monitoring and energy consumption compensation through other methods. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides an IoT-based method for optimizing energy consumption in wastewater treatment, the method comprising:

[0006] Monitor wastewater treatment equipment to optimize it;

[0007] Strategic planning is based on optimized wastewater treatment equipment and the type and volume of wastewater.

[0008] Wastewater is treated according to the wastewater treatment strategy, and the waste heat generated during the treatment is recovered to achieve energy consumption compensation.

[0009] Optionally, the process of monitoring wastewater treatment equipment and optimizing the equipment includes:

[0010] Obtain historical data from the wastewater treatment equipment;

[0011] The historical data was reduced in dimensionality using dynamic principal component analysis to obtain dimensionality-reduced data.

[0012] The Kantorovich distance is used to perform fault detection on the dimensionality-reduced data, and the fault detection results are used to optimize wastewater treatment equipment through Internet of Things (IoT) technology.

[0013] Optionally, the historical data can be reduced in dimensionality using dynamic principal component analysis (MPA). The process of obtaining the dimensionality-reduced data specifically includes:

[0014] Construct a feature augmentation matrix based on the historical data;

[0015] The feature augmented matrix is ​​standardized, and the covariance is calculated based on the standardized feature augmented matrix.

[0016] Based on the covariance and the principal component information contained in the historical data, data dimensionality reduction is performed to obtain dimensionality-reduced data.

[0017] Optionally, the standardization process for the feature augmentation matrix specifically includes:

[0018] X = Σ -1 (X z -sξ T )

[0019] Where s and Σ are X z The mean vector and standard deviation diagonal matrix of the eigenvalue augmented matrix, where ξ is a column vector with elements of 1.

[0020] Optionally, the strategic planning based on the optimized wastewater treatment equipment and the type and volume of wastewater specifically includes:

[0021] Wastewater is classified into two types: domestic sewage and industrial wastewater, and the flow rate and volume are predicted and measured separately for each type.

[0022] Based on the two types and measurement results, strategic planning is carried out for aeration systems, influent loads, and wastewater treatment stage transitions.

[0023] Optionally, based on the two types and measurement results, the specific content of wastewater treatment planning includes:

[0024] Aeration system setup: Based on the traditional aeration system, microfiltration and ultrafiltration membrane technologies are added to separate solid particles and microorganisms in industrial wastewater. For domestic sewage, rocker arm air aerators are used to separate particles and microorganisms.

[0025] Adjustment of influent load: Where V is the inflow rate per unit time, and t is time;

[0026] Wastewater treatment stage transition: Water quality sensors are used to monitor the water quality at the current stage, and the wastewater treatment stage transition is carried out according to the wastewater treatment standard range.

[0027] Optionally, energy consumption compensation through the recovery of waste heat includes:

[0028] Waste heat generated in the heat exchanger of the bioreactor and waste heat from the high-temperature water in the pretreatment process are recovered and used to compensate for the energy consumption of wastewater treatment through thermal energy conversion.

[0029] Optionally, the energy consumption compensation achieved by increasing the sludge settling rate specifically includes:

[0030] Adding packing particles to the sedimentation process allows the packing to form a biofilm on the sludge surface, enhancing adhesion, increasing settling speed, reducing energy consumption, and achieving energy compensation.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] This invention provides energy-saving optimization control for wastewater treatment processes, which can improve the overall efficiency and operational stability of equipment while reducing energy consumption. Through rationally designing the treatment process, scientifically regulating aeration volume, timely maintenance and replacement of aging equipment, utilizing waste heat, and employing energy-saving technologies and new materials, this invention helps wastewater treatment plants achieve a dual balance between energy consumption and energy utilization efficiency. This contributes to environmental protection while also saving costs for businesses. Attached Figure Description

[0033] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a diagram illustrating the method steps of an embodiment of the present invention. Detailed Implementation

[0035] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] IoT-based energy consumption optimization methods for wastewater treatment, such as Figure 1 As shown, the method includes:

[0037] Monitor wastewater treatment equipment to optimize it.

[0038] The process of monitoring and optimizing wastewater treatment equipment specifically includes: acquiring historical data of the wastewater treatment equipment; using dynamic principal component analysis to reduce the dimensionality of the historical data to obtain dimensionality-reduced data; using Kantorovich distance to perform fault detection on the dimensionality-reduced data; and using the fault detection results to optimize the wastewater treatment equipment through Internet of Things (IoT) technology.

[0039] The historical data of the wastewater treatment equipment includes: water quality data, pH value data, chlorine content, and turbidity. In this embodiment, the historical water quality data is collected using a BOD water quality analyzer; the historical pH value is obtained using a pH sensor or acid-base analyzer; the chlorine content is obtained by converting the detected hypochlorous acid concentration in the water into a current value through a diaphragm polarographic sensor with cathode and anode materials, which is then converted into a standard signal by the sensor's electronic circuitry; the historical turbidity data is obtained by calculating the ratio based on the multi-segment linear relationship between scattered light and turbidity after multi-point calibration.

[0040] The process of using dynamic principal component analysis to reduce the dimensionality of the historical data to obtain dimensionality-reduced data specifically includes: constructing a feature augmented matrix based on the historical data; standardizing the feature augmented matrix; calculating the covariance based on the standardized feature augmented matrix; and reducing the dimensionality of the data based on the covariance and the principal component information contained in the historical data to obtain dimensionality-reduced data.

[0041] In this embodiment, the parallel analysis method is used to calculate the lag length only when historical data is required, and the feature augmented matrix containing the static and dynamic features of the process is generated based on the lag length.

[0042] The standardization process for the feature augmentation matrix specifically includes:

[0043] X = Σ -1 (X z -sξ T )

[0044] Where s and Σ are X z The mean vector and standard deviation diagonal matrix of the eigenvalue augmented matrix, where ξ is a column vector with elements of 1.

[0045] The KD metric is used to calculate the distance between probabilistic measures of the standardized feature augmented matrices. The KD metric can be flexibly applied to data following any statistical distribution. The KD metric between two distributions is calculated using a piecewise process. Since comparing individual observations in the source and target distributions is a time-consuming process, as the data in both distributions are divided into multiple parts, the data designed for Kantorovich distance are stacked into segments, and then each segment in the source distribution is compared to obtain a smooth representation of the KD metric.

[0046] The method for fault detection using Kantorovich distance in the dimensionality-reduced data specifically includes:

[0047] The feature augmented matrix generated using dynamic principal component analysis (PCA) contains feature information. The KD metric is used to evaluate the difference between the training residuals and the test data during the processing. If the device malfunctions, information about the malfunction is reflected in the residuals. When the training and test data are similar, the residuals are similar, and the calculated KD value is smaller.

[0048] The detection process includes: taking a set of normal data and test data, and processing them with zero mean and unit variance respectively; calculating the lag number to obtain the augmented matrix; calculating the residuals based on the augmented matrix; calculating the mean and covariance of the residuals; calculating the KD value of the mean and covariance; determining whether there is a device malfunction based on the KD value; and if a malfunction is found, uploading the data to the host computer of the IoT device.

[0049] Strategic planning is based on optimized wastewater treatment equipment and the type and volume of wastewater.

[0050] The strategic planning based on the optimized wastewater treatment equipment and the type and volume of wastewater specifically includes: classifying wastewater into two types, domestic sewage and industrial wastewater, and predicting and measuring the flow and volume of each; and based on the two types and the measurement results, conducting strategic planning for the aeration system, influent load, and wastewater treatment stage transitions.

[0051] Based on the two types and measurement results, the specific content of the wastewater treatment plan includes: Aeration system setup: In addition to traditional aeration systems, microfiltration and ultrafiltration membrane technologies are added to separate solid particles and microorganisms from industrial wastewater. For domestic wastewater, rocker-arm air aerators are used to separate particles and microorganisms. Applying membrane separation technologies such as microfiltration and ultrafiltration to wastewater can effectively separate solid particles and microorganisms, thereby improving treatment efficiency. Microfiltration membranes have pore sizes of 0.1-10 μm, effectively removing suspended particles, colloidal particles, and large particles; ultrafiltration membranes have pore sizes of 0.001-0.1 μm, effectively removing tiny particles such as colloids, bacteria, and viruses. The aerators were optimized by increasing their ventilation performance, increasing the number of ventilation holes, and adjusting their layout. The ventilation of the aeration device is a crucial indicator affecting the aeration efficiency of the aeration system; therefore, using a high-ventilation aeration device can effectively improve the oxygen transfer efficiency of the aeration system and improve the mixing degree in the aquatic environment. By increasing the number of aeration holes and adjusting the layout of the aeration sills, a more uniform oxygen supply to the water can be achieved, thereby improving the oxidizing power of the reactor. The operating parameters of the aeration system were optimized to achieve better results. Commonly used operating parameters include aeration time, aeration intensity, and aeration duration. Based on this, a new enhanced process was proposed, which, under certain conditions, strengthens the oxidizing power of the bioreactor and effectively degrades organic pollutants in the wastewater by appropriately increasing the aeration time and aeration intensity.

[0052] Adjustment of influent load: Where V is the inflow rate per unit time, and t is time;

[0053] Wastewater treatment stage transition: Water quality sensors are used to monitor the water quality at the current stage, and the wastewater treatment stage transition is carried out according to the wastewater treatment standard range. This embodiment uses ultraviolet light oxidation to degrade organic pollutants in the water.

[0054] Wastewater is treated according to the wastewater treatment strategy, and the waste heat generated during the treatment is recovered to achieve energy consumption compensation.

[0055] Energy consumption compensation through waste heat recovery includes: recovering waste heat generated by heat exchangers in bioreactors and waste heat from high-temperature water in the pretreatment process, and compensating for the energy consumption of wastewater treatment through thermal energy conversion.

[0056] In this embodiment, solar energy, wind energy, and thermal energy can also be used together for energy consumption compensation.

[0057] Energy consumption compensation by increasing sludge settling rate includes adding packing particles to the sedimentation process. The packing particles form a biofilm on the sludge surface, which enhances adhesion, increases settling speed, and reduces energy consumption, thus achieving energy consumption compensation.

[0058] In this embodiment, the values ​​monitored by the sensors are transmitted and displayed using Internet of Things (IoT) technology. The IoT technology employs an end-to-end authentication mechanism and the MQTT transmission protocol. The reason for using the MQTT IoT communication protocol is that the client receiving the message can be a sensor, which can then process the received message; if it is a program, the program can be run.

[0059] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A wastewater treatment energy consumption optimization method based on the Internet of Things, characterized in that, The method includes: Monitor wastewater treatment equipment to optimize it; Strategic planning is based on optimized wastewater treatment equipment and the type and volume of wastewater. Wastewater is treated according to the wastewater treatment strategy, the waste heat generated during treatment is recovered, and advanced oxidation technology is used to improve sludge settling technology. Energy consumption is compensated by recovering waste heat and increasing sludge settling rate. The process of monitoring wastewater treatment equipment and optimizing it specifically includes: Obtain historical data from the wastewater treatment equipment; The historical data was reduced in dimensionality using dynamic principal component analysis to obtain dimensionality-reduced data. The Kantorovich distance is used to perform fault detection on the dimensionality-reduced data, and the fault detection results are used to optimize wastewater treatment equipment through Internet of Things (IoT) technology. The method for fault detection using the Kantorovich distance in the dimensionality-reduced data specifically includes: The feature augmented matrix generated using the dynamic principal component analysis method has feature information. The KD index is used to evaluate the difference between the training residuals and the test data during the processing. If the equipment malfunctions, the information about the malfunction will be reflected in the residuals. When the training data and test data are close, the residuals are similar and the calculated KD value is small. The process of using dynamic principal component analysis to reduce the dimensionality of the historical data to obtain dimensionality-reduced data specifically includes: Construct a feature augmentation matrix based on the historical data; The feature augmented matrix is ​​standardized, and the covariance is calculated based on the standardized feature augmented matrix. Based on the covariance and the principal component information contained in the historical data, data dimensionality reduction is performed to obtain dimensionality-reduced data. The strategic planning based on optimized wastewater treatment equipment and the type and volume of wastewater specifically includes: Wastewater is classified into two types: domestic sewage and industrial wastewater, and the flow rate and volume are predicted and measured separately for each type. Based on the two types and measurement results, strategic planning is carried out for aeration systems, influent loads, and wastewater treatment stage transitions.

2. The wastewater treatment energy consumption optimization method based on the Internet of Things according to claim 1, characterized in that, Energy compensation through waste heat recovery includes: Waste heat generated in the heat exchanger of the bioreactor and waste heat from the high-temperature water in the pretreatment process are recovered and used to compensate for the energy consumption of wastewater treatment through thermal energy conversion.

3. The wastewater treatment energy consumption optimization method based on the Internet of Things according to claim 1, characterized in that, Energy compensation through increasing sludge settling rate specifically includes: Adding packing particles to the sedimentation process allows the packing to form a biofilm on the sludge surface, enhancing adhesion, increasing settling speed, reducing energy consumption, and achieving energy compensation.

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