Editor's Note: Sensors, as the "nerve endings of the information age," have penetrated every critical field of the socio-economic landscape. Since October 2025, China Electronic News has invited Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Jiusan Society Central Committee and Executive Vice Chairman of the China Sensor and IoT Industry Alliance, to launch the column "Guo Yuansheng Explains Sensors." The column focuses on eight major fields and scenarios: electric power, major equipment, intelligent manufacturing, smart agriculture, smart healthcare and big health, smart home appliances and consumer electronics, urban security, and low-altitude economy. Articles such as "Sensors 'Stationed' on the Power Generation Side: The Cornerstone of Stable Operation in New Power Systems" and "Energy Storage Sensors Clarify Three Core Development Directions" have been published successively, receiving widespread attention and high praise from readers. This article focuses on intelligent sensors in the fine chemical industry, elaborating on their application prospects, industrial status, and future recommendations to build industrial consensus and promote industrial development.
As a high-end extension of the petrochemical industry, fine chemicals serve as a key benchmark for measuring a country's comprehensive competitiveness in the petrochemical sector. It is characterized by "multiple varieties, small batches, complex processes, harsh reactions, high product added value, and strict quality control." The products cover multiple sub-sectors such as pharmaceutical intermediates, electronic chemicals, agrochemicals, and the coatings industry. The production process demands ultimate control precision for critical process parameters such as temperature, pressure, flow rate, composition, and purity. Therefore, stringent parameter control is the core to ensuring product quality, stable capacity, and enhanced profitability.
Currently, China's fine chemical industry is in a critical period of high-quality development, tackling intelligent transformation and deepening green transformation. Meanwhile, frontier technologies such as AI algorithms, IoT (Internet of Things) technologies, and high-performance intelligent sensing technologies are accelerating their iterative upgrades. Their deep integration with the fine chemical industry is not only the key approach to solving core pain points like insufficient digitalization of production processes, inaccurate data collection, and unintelligent production scheduling, but also the core path to driving the industry's transformation from "experience-driven" to "data-driven" and comprehensively enhancing intelligent production levels and core competitiveness.
I. Characteristics of the Fine Chemical Industry and Core Requirements for Sensors
Underlying Requirements for Sensors Driven by Industry Differentiation Characteristics
The fine chemical industry presents an industrial pattern of "large-scale layout by large enterprises and customized breakthroughs by small and medium-sized enterprises (SMEs)." The production models of these two scales of enterprises are completely different, leading to significant differences in sensor configuration needs and performance requirements.
1. Differences in Production Scale: Large Enterprises vs. SMEs
Large fine chemical enterprises mainly focus on the large-scale production of high-value-added general fine chemical products, featuring MDI, coating resins, bulk pharmaceutical intermediates, and general pesticides. A single production line has large capacity and long continuous operation cycles, imposing extremely high requirements on the long-term stability and batch compatibility of sensors. A single production line of such enterprises requires 500-800 supporting sensors, with core requirements including: continuous stable working time ≥2 years without frequent replacement and calibration; sensor data from different production lines and equipment can be uniformly accessed into the enterprise MES system to achieve data collaboration and centralized control; and the ability to resist harsh environments, adapting to long-term high-temperature, high-pressure, and continuous reaction production scenarios.
SMEs in the fine chemical industry mainly focus on small-batch customized production, featuring high-end customized pharmaceutical intermediates, specialized electronic chemicals, and special coatings. The output per batch is mostly 50-100kg, and product variety switching on production lines is frequent. Thus, the requirements for the rapid calibration capability and high precision in small ranges of sensors are more prominent. A single production line of such enterprises is equipped with 100-300 sensors, with core requirements including: calibration time ≤2 hours during variety switching to quickly adapt to the process parameter requirements of different products; small-range measurement accuracy meets standards, e.g., when the flow measurement range is 0-5m³/h, the measurement error to expand from ±0.25% FS to ±1% FS; compact size and convenient installation to adapt to the installation scenarios of small reactors and purification equipment; and low maintenance costs to match the financial capabilities of SMEs.
2. Process Differences in Sub-sectors: Exclusive Requirements for Four Core Fields
There are numerous sub-sectors in fine chemicals, among which pharmaceutical intermediates, electronic chemicals, agrochemicals, and the coatings industry are the four core fields. The particularity of their production processes determines that the technical requirements for sensors are highly exclusive and cannot be universally adapted, which is the core embodiment of the differentiated demand for fine chemical sensors.
(1) Pharmaceutical Intermediates Field: The production process mostly involves liquid-phase catalytic reactions with harsh conditions. It is necessary to strictly control three core parameters: temperature, pH value, and dissolved oxygen concentration. The reaction media are mostly organic solvents such as ethanol, acetone, and DMF. Therefore, sensors must possess strong resistance to organic solvent corrosion while meeting high-precision control requirements. Temperature error ≤±0.5℃, pH error ≤±0.1, dissolved oxygen concentration error ≤±0.2mg/L, to avoid product yield decline and unqualified purity caused by parameter deviation.
(2) Electronic Chemicals Field (e.g., photoresists, electronic-grade hydrogen peroxide, electronic-grade ammonia): Products are extremely sensitive to impurity content. The content of metal ion impurities and micro-particle impurities must be controlled at the ppb level. Therefore, sensors must have ultra-high purity compatibility. Materials must adopt special materials like PTFE and quartz that do not precipitate metal ions. Meanwhile, they must possess ultra-high-precision purity and particle detection capabilities to avoid secondary pollution of electronic chemicals caused by the sensors themselves.
(3) Agrochemicals Field: The production process involves strong acid and strong alkali media such as concentrated sulfuric acid and sodium hydroxide solutions. Some processes involve high-temperature and high-pressure reaction scenarios with high risk levels. Therefore, sensors must have extremely strong corrosion resistance, with a protection rating of IP68 or higher. Key components like diaphragms and shells must adopt corrosion-resistant special alloys such as Hastelloy C-276. They must also have high-precision pressure, liquid level, and concentration detection capabilities to prevent safety risks like reactor leakage and medium exceedance.
(4) Coatings Industry Field: The production process requires monitoring parameters such as viscosity, solid content, and temperature. Coating media have high viscosity, are easy to adhere to, and prone to scaling. If the sensor surface scales, it will seriously affect measurement accuracy. Therefore, sensors must have strong anti-scaling capabilities while meeting accuracy requirements of viscosity error ≤±5mPa·s and solid content error ≤±1%, adapting to the variety switching needs of water-based and oil-based coatings.
Core Requirement List for Sensors in Different Sub-sectors
To more clearly present the sensor demand differences in the four core sub-sectors, combined with the usage configuration of a single production line, a core requirement list is compiled to provide precise references for selection and product R&D:
Note: This list covers core sensor usage and does not include auxiliary monitoring sensors (e.g., ambient temperature and humidity sensors); the usage for large enterprise production lines takes the upper limit, while that for SMEs takes the lower limit.
II. Application Status of Traditional Sensors in Fine Chemicals
Currently, sensor applications in China's fine chemical industry are still dominated by traditional sensors, with the penetration rate of high-end intelligent sensors being less than 10%, mainly concentrated in the high-end production lines of large enterprises. Although traditional sensors can meet basic real-time monitoring needs and support the normal operation of production lines, looking at application cases in the four sub-sectors, their core shortcomings in technical performance, adaptability, data utilization, and maintenance costs are increasingly prominent, becoming an important bottleneck restricting the high-end upgrading of the fine chemical industry.
Application Cases and Usage Analysis of Core Sensors
Selecting the four sub-sectors and combining typical enterprise cases, this section analyzes in detail their application status, technical parameters, usage costs, and core pain points.
1. Pharmaceutical Intermediate Production: Online pH Sensors
Mainly used for real-time monitoring of the pH value of reaction liquids in cephalosporin and penicillin intermediates, its measurement accuracy directly determines product yield. The optimal pH range is 6.5-7.0. If the pH value deviates by 0.5, it will lead to a product yield decline of over 10%, or even generate by-products, causing raw material waste.
Traditional Products: Currently, glass electrode pH sensors are widely used in SME pharmaceutical intermediate plants. The material is sensitive membrane special glass with a PP plastic shell, making it the most cost-effective basic monitoring equipment.
Technical Parameters: Measurement range 0-14pH, accuracy ±0.1pH, response time ≤5s, working temperature 0-80℃, capable of withstanding low-concentration organic solvents like ethanol and acetone.
Usage and Cost: A pharmaceutical plant has 5 cephalosporin intermediate production lines, deploying 20 sensors in total. The procurement cost per unit is about 8,000 RMB, and the annual maintenance cost is about 1,500 RMB/unit, mainly for replacing the electrode protective liquid every 3 months and cleaning attachments on the electrode surface.
Pain Points: Glass electrodes are easily corroded and aged by organic media, with a service life of only 6-8 months, requiring frequent replacement. Each replacement requires shutdown, disassembly, and calibration, leading to about 2 hours of downtime per month and a loss of about 50,000 RMB per batch. The electrode surface easily adheres to reaction residues, requiring manual regular cleaning, which increases labor costs, and untimely cleaning leads to increased measurement errors.
2. Electronic Chemicals Production: Laser Particle Sensors
This sensor is the core equipment in the purification process of electronic chemicals like electronic-grade hydrogen peroxide and photoresists. It is mainly used to detect micro-particle impurities in the medium, requiring the number of particles ≥0.5μm to be ≤10/mL. If the particle size exceeds the standard, it will lead to unqualified purity, failing to meet the production needs of semiconductor chips.
Traditional Products: The mainstream adopts optical blocking laser particle sensors. The light source is a semiconductor laser with a wavelength of 650nm. The technology is mature and detection speed is fast.
Technical Parameters: Detection range 0.1-100μm, counting accuracy ±10%, sampling flow rate 20mL/min, working temperature 20-30℃. It needs to be in a constant temperature environment; temperature fluctuations >±2℃ will seriously affect detection accuracy.
Usage and Cost: An electronic chemical plant has 2 electronic-grade hydrogen peroxide purification production lines, deploying 8 such sensors. The procurement cost per unit is about 120,000 RMB. It requires a supporting constant temperature cabinet (additional cost of 20,000 RMB/unit), and the annual calibration cost is about 20,000 RMB/unit. Calibration requires professional personnel to operate on-site.
Pain Points: Extremely sensitive to ambient temperature. When workshop air conditioning fails or temperature fluctuates, false alarms for particle size exceedance are prone to occur. The deployment of constant temperature cabinets increases equipment investment and energy consumption costs. Annual calibration costs are high, and the long calibration cycle affects the continuous operation of production lines. The material has a risk of trace metal ion precipitation, which may cause secondary pollution to electronic chemicals.
3. Agrochemicals Production: Corrosion-Resistant Pressure Transmitters
Used for pressure monitoring in the synthesis reactors of pesticides like glyphosate and imidacloprid. The normal pressure is 0.3-0.5MPa. If the pressure exceeds 0.6MPa, it can lead to reactor leakage and reaction runaway, triggering safety accidents.
Traditional Products: The mainstream adopts capacitive corrosion-resistant pressure transmitters. The diaphragm material is Hastelloy C-276, and the filling liquid is fluorine oil, possessing certain corrosion resistance.
Technical Parameters: Measurement range 0-1MPa, accuracy ±0.25% FS, working temperature -20-120℃, protection rating IP68, capable of withstanding 30% concentrated sulfuric acid corrosion.
Usage and Cost: A large agrochemical plant has 10 glyphosate production lines, deploying 60 sensors. The procurement cost per unit is about 15,000 RMB, the service life is about 18 months, the annual replacement cost is about 450,000 RMB, and manual calibration is required once a month.
Pain Points: The fluorine oil filling liquid is easy to age and deteriorate at high temperatures (>80℃), causing the measurement error to expand from ±0.25% FS to ±1% FS, affecting the accuracy of pressure monitoring. Manual calibration takes 1 hour per unit per month, with annual calibration man-hours of about 720 hours, resulting in high labor costs. The diaphragm is easily corroded by strong acid media over the long term, resulting in a short service life. Shutdown is required for replacement, affecting production progress.
4. Coatings Industry Production: Online Viscometers
The online viscometer is the core monitoring equipment in coating production, mainly used to monitor the viscosity parameters of coatings. The viscosity ranges vary greatly for different types of coatings. If the viscosity deviation is too large, it will lead to insufficient coating adhesion, uneven color, poor construction performance, and an increased rejection rate.
Traditional Products: The mainstream adopts rotary online viscometers. The material is stainless steel or ordinary PTFE, adapting to the monitoring of a single type of coating.
Technical Parameters: Measurement range 100-1000mPa·s, accuracy ±5mPa·s, working temperature 0-90℃, response time ≤8s.
Usage and Cost: A small coating plant has 3 water-based coating + 2 oil-based coating production lines, deploying 35 sensors. The procurement cost per unit is about 6,000 RMB, and the annual maintenance cost is about 1,000 RMB/unit, mainly for cleaning surface scaling and replacing rotary blades.
Pain Points: Weak anti-scaling ability; coatings easily adhere to the sensor blades, leading to decreased accuracy. Water-based and oil-based coatings require replacing viscometers with different materials, taking 4 hours for variety switching, seriously affecting production efficiency. Blades are easy to wear, with a service life of only 12 months, requiring frequent replacement and relatively high costs.
Common Shortcomings in Traditional Sensor Applications
1. Insufficient adaptability, difficult to match the needs of multi-variety production switching. The core characteristic of fine chemicals is "multiple varieties, small batches." A single production line often needs to switch between multiple products, while traditional sensors are mostly designed for "single adaptation," only able to adapt to the monitoring needs of one product and one medium.
2. Weak anti-interference ability, high failure rate, and insufficient stability. The fine chemical production environment is harsh. Environmental factors such as high temperature, high pressure, strong corrosion, and high humidity, as well as the viscosity, corrosiveness, and volatility of the media, will seriously affect the measurement accuracy and service life of traditional sensors.
3. Single data utilization, remaining only at the "monitoring level," unable to achieve process optimization. The core function of traditional sensors is "real-time monitoring + over-limit alarm." The collected data can only be used to judge whether process parameters meet standards, and cannot be combined with production process data for in-depth analysis, trend prediction, and process optimization.
III. Integration of Frontier Technologies and Sensors: Solving Intelligent Pain Points in Fine Chemicals
AI Algorithms + Traditional Sensors: Enhancing Precision in Small-Batch Production
The advantages of AI algorithms are "data compensation, interference identification, and trend prediction." Integrated with the monitoring functions of traditional sensors, they can solve the core pain points of insufficient measurement accuracy, weak anti-interference ability, and high false alarm rate without replacing sensor hardware, only through algorithm optimization. This is especially suitable for the small-batch customized production needs of SMEs, offering high cost-effectiveness and serving as the "low-cost breakthrough path" for the current intelligent upgrade of sensors.
1. Data Compensation for Pharmaceutical Intermediate pH Sensors Based on LSTM
Addressing the pain points of traditional glass electrode pH sensors being easily affected by temperature and organic media, resulting in large measurement errors and short service life, the time-series analysis capability of LSTM neural networks is utilized to perform real-time compensation on the data collected by pH sensors, achieving high-precision monitoring and extended service life.
Technical Solution: First, collect 3 years of historical data from a pharmaceutical intermediate plant, covering 100,000 records including pH value, reaction temperature, reaction liquid composition, sensor service life, and medium concentration. Label normal data and interfered data (e.g., pH deviation when temperature fluctuates ±3℃, pH deviation caused by organic media corrosion). Second, train the LSTM neural network model, taking real-time temperature and original pH value as input parameters to output the compensated accurate pH value. Meanwhile, identify the aging trend of the sensor through the model to warn of replacement time in advance. Finally, embed the model into the sensor's control module to realize integrated functions of real-time data compensation, aging warning, and over-limit alarm.
Application Effects: A medium-sized pharmaceutical intermediate plant deployed AI-compensated pH sensors on cephalosporin intermediate production lines: pH measurement accuracy improved from ±0.1 to ±0.05, far exceeding production requirements; sensor service life extended from 6-8 months to 12 months, with replacement frequency reduced by 50%; monthly downtime for replacement reduced to 0.4 hours, reducing annual downtime losses by about 300,000 RMB; through trend analysis of pH value data, reaction time was adjusted, product yield increased by 2%, and annual new benefits reached about 500,000 RMB.
2. Optimizing Environmental Adaptability of Laser Particle Sensors with Random Forest Algorithm
Addressing the pain points of traditional laser particle sensors being sensitive to temperature, having high false alarm rates, and requiring constant temperature cabinets, the random forest algorithm is used to identify temperature interference features and perform real-time correction on particle detection data to eliminate temperature interference, enhancing the environmental adaptability of sensors and reducing equipment investment costs.
Technical Solution: First, deploy temperature and laser particle sensor linkage on the chemical purification production line, collecting 50,000 corresponding data records of temperature fluctuations (±0-5℃) and particle detection values to sort out the interference law of temperature fluctuations on particle detection. Second, train the random forest algorithm model. Through the model, identify interference features under different temperature fluctuations. When the temperature fluctuation exceeds ±2℃, automatically correct the particle detection value to eliminate temperature interference and avoid false alarms. Finally, optimize the sensor's detection logic. Without a constant temperature cabinet, high-precision detection in a 10-40℃ environment can be achieved only through algorithm compensation.
Application Effects: An electronic chemical plant deployed AI-optimized sensors with significant results: the false alarm rate for particle detection dropped from 3-4 times per month to 0, avoiding production shutdown and investigation losses of about 800,000 RMB/year caused by false alarms; no constant temperature cabinet is needed, reducing the cost per unit by 20,000 RMB, saving a total of 160,000 RMB in constant temperature cabinet costs for 8 sensors; annual calibration costs reduced by 50%, detection accuracy improved to 99.8%, and product qualification rate increased by 3%.
IoT + Multiple Sensors: Achieving Collaborative Monitoring for Multi-Variety Production
1. Integration of Multi-Parameter Sensor Networks and MES Systems in Coating Plants
Addressing the pain points of slow sensor calibration, disconnected data, and low switching efficiency, a multi-parameter sensor network is built and deeply integrated with the enterprise MES system to achieve collaborative monitoring, automatic calibration, and rapid variety switching in coating production.
Technical Solution: Deploy 100 online viscometers, infrared solid content sensors, platinum resistance thermometers, etc., on the coating production line, networked via LoRa modules to adapt to the complex environment of the coating workshop, with data uploaded to the MES system in real time. Second, establish a process parameter library for different coating varieties in the MES system, clarifying the core parameter ranges such as viscosity, solid content, and temperature for different products like water-based, oil-based, and special coatings, as well as sensor calibration parameters. Finally, realize automated variety switching. When the production line switches products, the MES system automatically calls the corresponding product's process and calibration parameters, and the sensors automatically complete calibration without manual operation. Meanwhile, multi-parameter data collaborative analysis is realized to optimize process parameters like stirring speed and feeding ratio.
Application Effects: After deploying the sensor network, a large coating plant significantly improved production efficiency and product quality: variety switching time shortened from 4 hours to 1 hour, with production efficiency increased by 30%; calibration accuracy reached 100%, requiring no manual calibration, saving about 500 calibration man-hours annually; the coating rejection rate dropped from 5% to 1%, reducing losses from unqualified products by about 1.2 million RMB annually; data utilization rate increased to 25%. Through multi-parameter collaborative analysis to optimize process parameters, coating adhesion improved by 15%, and construction performance significantly improved.
2. Cloud Platform for Corrosion-Resistant Sensors in Agrochemical Plants: Remote Monitoring and Maintenance
Addressing the pain points of monthly manual calibration, high maintenance costs, and high safety risks, a cloud platform for corrosion-resistant sensors is built, connecting all corrosion-resistant sensors to the cloud to achieve remote monitoring, automatic calibration, aging warning, and remote maintenance, reducing labor costs and preventing safety risks.
Technical Solution: First, connect 60 corrosion-resistant pressure transmitters, along with core sensors like liquid level and concentration sensors from a large agrochemical plant, to the cloud monitoring platform, uploading pressure data, equipment temperature, battery level, and measurement errors in real time. Second, the platform has a built-in automatic calibration model. Based on data such as sensor usage time, medium temperature, and measurement error, it automatically completes remote calibration. For example, if the sensor error increases by 0.2% after 6 months of use, the platform automatically sends a calibration command without manual on-site operation. Finally, build an aging warning system. By analyzing the sensor's operating status through the cloud platform, it warns in advance of the sensor's aging and fault trends, reminding staff to replace them in time to prevent safety risks like reactor leakage.
Application Effects: The agrochemical plant achieved significant results after deployment: calibration man-hours dropped from 720 hours per year to 0, saving about 144,000 RMB/year in labor costs; pressure measurement error stabilized within ±0.25% FS, reducing the risk of reactor leakage by 90%; sensor service life extended to 24 months, with annual replacement costs reduced by 33%; staff can remotely monitor the pressure status of production lines via mobile phones and computers without on-site inspections, reducing safety risks brought by strong acid and strong alkali media.
New Material Sensors: Breaking Through Limitations in Strong Corrosion and High Purity Scenarios
Traditional sensor materials lack sufficient performance to adapt to extreme production scenarios in fine chemicals such as strong corrosion, high purity, and high viscosity. Through material innovation, it is possible to achieve leapfrog improvements in sensor corrosion resistance, stability, and purity compatibility, meeting the stringent needs of high-end sub-sectors like electronic chemicals and pharmaceutical intermediates. This is the core path for the high-end upgrade of sensors.
1. Application of Silicon Carbide (SiC) pH Sensors in Pharmaceutical Intermediates
Silicon Carbide (SiC) is a new type of special ceramic material with extremely strong resistance to organic solvent corrosion, high-temperature resistance, and wear resistance. Its corrosion resistance far exceeds that of traditional glass electrodes and PP plastics. It is an ideal material for pH sensors in the pharmaceutical intermediate field, completely solving the pain points of traditional glass electrodes being easily corroded and having a short service life.
Technical Advantages: SiC material can withstand long-term corrosion by various strong organic solvents like ethanol, acetone, and DMF, with a service life of 24 months, 3 times that of traditional glass electrode pH sensors. It has strong high-temperature resistance, with a working temperature range of 0-100℃, adapting to the monitoring needs of high-temperature intermediate synthesis. The response speed is fast, with a response time ≤3s, 40% faster than traditional sensors. The measurement accuracy is high, with pH accuracy reaching ±0.05pH, far exceeding the accuracy requirements of traditional sensors.
Technical Parameters: Measurement range 0-14pH, accuracy ±0.05pH, response time ≤3s, working temperature 0-100℃, protection rating IP67, capable of withstanding various organic solvents. The sensor shell adopts SiC ceramic, and the sensitive element adopts an SiC thin film, with no aging or corrosion.
Application Case: A large pharmaceutical intermediate plant replaced 20 traditional glass electrode pH sensors entirely with SiC pH sensors, applying them to the monitoring of synthesis reactors for cephalosporin and penicillin intermediates with significant results: annual replacement costs dropped from 30,000 RMB to 10,000 RMB, with replacement frequency reduced by 67%; downtime for replacement reduced by 80%, reducing annual downtime losses by about 240,000 RMB; product yield steadily increased by 2%, and purity increased by 1.5%. There is no need for frequent manual cleaning of attachments on the electrode surface, saving about 30,000 RMB in labor costs annually, realizing integrated monitoring of "high precision, long life, and low maintenance."
2. Application of Quartz Laser Particle Sensors in Electronic Chemicals
Quartz material has core advantages of no metal ion precipitation, resistance to temperature fluctuations, and corrosion resistance, perfectly adapting to the high-purity monitoring needs of electronic chemicals. It avoids the secondary pollution problem of metal ion precipitation from traditional laser particle sensors and solves the pain point of traditional sensors being sensitive to temperature, requiring no constant temperature cabinet and reducing equipment investment and maintenance costs.
Technical Advantages: Avoids secondary pollution to electronic chemicals like electronic-grade hydrogen peroxide and photoresists, with purity compatibility reaching the ppb level. Strong resistance to temperature fluctuations, with detection accuracy unchanged in a ±5℃ environment, working temperature range 10-40℃, requiring no constant temperature cabinet. Particle counting accuracy is ±5%, 50% higher than traditional sensors. Service life reaches 36 months.
Technical Parameters: Detection range 0.1-100μm, counting accuracy ±5%, sampling flow rate 20mL/min, working temperature 10-40℃, protection rating IP66. The material is high-purity quartz with no metal ion precipitation, adapting to the high-purity purification monitoring of electronic chemicals.
Application Case: An electronic chemical plant replaced 8 traditional laser particle sensors with quartz laser particle sensors, applying them to the purification process monitoring of electronic-grade hydrogen peroxide with significant results: saved 160,000 RMB in constant temperature cabinet costs; annual calibration costs dropped from 160,000 RMB to 40,000 RMB, a 75% reduction in calibration costs; particle detection accuracy reached 100%, and the qualification rate of electronic chemical products increased to 99.9%; sensor service life extended to 36 months, with annual replacement costs reduced by 50%, completely solving the secondary pollution and false alarm problems of traditional sensors, adapting to the stringent purity requirements of electronic chemicals for semiconductor chips.
IV. Market Size and Usage Forecast for Fine Chemical Sensors
Global and Chinese Market Size
1. Market Status
In 2024, global fine chemical sensors showed a steady growth trend, with a market size of about 3.25 billion USD, a year-on-year growth of 7.8%. The growth momentum mainly comes from the demand growth in high-end fine chemical fields in countries and regions such as China, North America, and Europe. Among them, the demand growth rate for pharmaceutical intermediate sensors reached 9.2%, making it the fastest-growing sub-sector; the demand growth rate for electronic chemical sensors reached 8.7%, closely following; the demand growth rates for agrochemical and coatings industry sensors were 6.3% and 5.9% respectively, with relatively moderate growth.
As one of the world's largest producers and consumers of fine chemicals, China's fine chemical sensor market size was about 860 million USD in 2024, accounting for 26.5% of the global market. The year-on-year growth was 9.5%, far exceeding the global average, becoming the core engine of global market growth.
2. Breakdown of Core Growth Drivers
The growth rate of China's fine chemical sensor market far exceeds the global level, mainly benefiting from three core growth drivers. This growth momentum is not a short-term pulse but a long-term sustainable structural growth, aligning with the core orientation of future high-end manufacturing upgrading and green, low-carbon development, and will support the continuous high-speed growth of the market from 2024 to 2030.
First, policy-driven: Under policy guidance, enterprises have a strong willingness for intelligent transformation, becoming the core driver of sensor demand growth. Meanwhile, environmental control is becoming stricter. Fields like pesticides and coatings are required to mandatorily monitor parameters such as VOCs emissions, wastewater pH, and medium concentration, driving the rapid growth of environmental sensor demand. In 2024, environmental sensors accounted for 15% of China's fine chemical sensor market, with a scale of about 129 million USD.
Second, high-end demand-driven: In 2024, the localization rate of electronic chemicals reached 35%, an increase of 15% compared to 2005; the localization rate of high-end pharmaceutical intermediates reached 42%, an increase of 18% compared to 2005. High-end fine chemical products have extremely high requirements for the accuracy, corrosion resistance, and purity compatibility of intelligent sensors. The unit price of high-end sensors is 2-3 times that of traditional products. The demand growth for high-end products directly drives the rapid expansion of market size.
Third, cost optimization-driven: With the popularization of AI algorithms and IoT technologies, as well as the scaled production of new materials, the production costs of intelligent sensors are gradually declining. For example, the cost of SiC pH sensors is expected to drop by 30% by 2027, and the cost of AI sensors will drop by 25%. The cost reduction enables SMEs to gradually expand the procurement scale of intelligent sensors, becoming an important supplement to market growth.
Sensor Usage Proportion and Growth Forecast in Various Sub-sectors
Forecasting the usage growth trend of sensors in various sub-sectors from 2024 to 2027, electronic chemicals and pharmaceutical intermediates have the highest compound growth rates, becoming the core sub-sectors for usage growth.
Note: This usage statistics covers core sensor usage and does not include auxiliary monitoring sensors; the 2027 usage forecast is based on the current capacity expansion speed and intelligent transformation progress. If policies are intensified or technological breakthroughs accelerate, usage may exceed the forecasted values.
Market Penetration of Frontier Sensors
It is estimated that by 2027, the penetration rate of frontier sensors will achieve doubled growth, gradually replacing traditional sensors and becoming mainstream products in high-end sub-sectors.
1. AI-Integrated Sensors
In 2024, the market size of AI-integrated sensors in China was about 90 million USD, mainly applied in pharmaceutical intermediates (40%) and electronic chemicals (30%), with the rest applied in agrochemicals, coatings industry, and other fields. Currently, the penetration rate of AI-integrated sensors presents a pattern of "large enterprises leading, SMEs lagging behind": the penetration rate in large fine chemical enterprises reaches 20%, widely applied in parameter compensation, false alarm elimination, and trend prediction for core production lines; the penetration rate in SMEs is only 5%, mainly deployed in core links due to cost factors.
It is estimated that by 2027, the domestic market size of AI-integrated sensors will reach 180 million USD, achieving doubled growth, with the penetration rate significantly increasing: the penetration rate in the electronic chemical field will reach 35%, and in the pharmaceutical intermediate field will reach 30%; the penetration rates in the agrochemical and coatings industry fields will reach 18% and 15% respectively; the penetration path will extend from large enterprises to SMEs. With cost reductions, SMEs will gradually expand the procurement scale of AI-integrated sensors, realizing "low-cost intelligent upgrading."
2. New Material Sensors (SiC, Quartz, etc.)
In 2024, the market size of new material sensors in China was about 70 million USD, mainly applied in high-end sub-sectors like electronic chemicals and high-end pharmaceutical intermediates. Due to higher costs (the unit price of SiC pH sensors is about 15,000 RMB, 1.8 times that of traditional products; the unit price of quartz laser particle sensors is about 150,000 RMB, 1.25 times that of traditional products), the penetration rate is relatively low: the penetration rate in the electronic chemical field reaches 15%, in the pharmaceutical intermediate field reaches 10%, and in the agrochemical and coatings industry fields is less than 5%.
It is estimated that by 2027, with the scaled production of new materials, the cost of new material sensors will drop by 30%, and the market size will reach 160 million USD, with the penetration rate achieving doubled growth: the penetration rate in the electronic chemical field will increase to 30%, becoming the mainstream sensor type; the penetration rate in the pharmaceutical intermediate field will increase to 20%, gradually replacing traditional glass electrode pH sensors and fluorescent dissolved oxygen sensors; the penetration rate in the agrochemical field will increase to 12%, mainly used for monitoring strong acid and strong alkali media; the penetration rate in the coatings industry field will increase to 10%, used for monitoring viscosity and solid content of special coatings.
V. Current Status and Bottlenecks of Intelligentization Level of Fine Chemical Sensors
With the technological breakthroughs and market penetration of intelligent sensors, the intelligentization level of sensors in China's fine chemical industry is gradually improving. However, due to differences in enterprise scale, technological investment, and talent reserves, the intelligent development of the industry presents a "polarized" pattern. Meanwhile, whether large or SMEs, they all face multiple bottlenecks such as technology, cost, standards, and talent. These bottlenecks cannot be solved by a single enterprise and require collaborative efforts from the industry, enterprises, and research institutes to achieve breakthroughs.
Intelligentization Differences Among Enterprises of Different Scales
1. Large Fine Chemical Enterprises: Leading in Intelligentization Pilots, Gradually Achieving Scaled Promotion
Large fine chemical enterprises have abundant financial strength, sufficient technological reserves, and a complete talent team. They are the "pioneers" of sensor intelligentization upgrades. Their intelligent development direction aligns with the future requirements of high-end and intelligent transformation in fine chemicals. Currently, their intelligentization level has reached a high level, gradually realizing full-process intelligent control of "monitoring + optimization + maintenance + safety."
(1) Sensor networking rate: Reaches 75%-85%, with the networking rate of core production lines exceeding 90%;
(2) Intelligent applications: Widely piloted and promoted technologies such as AI data compensation, cloud platform remote maintenance, and digital twin process simulation;
(3) Data utilization rate: About 30%, with a large amount of monitoring data used for process optimization, trend prediction, and fault warning;
(4) Financial and technological investment: Annual intelligent transformation investment exceeds 500 million RMB, equipped with professional IoT and AI technology teams (scale of 50-100 people), cooperating with universities and research institutes to carry out frontier sensor technology R&D.
2. SMEs in Fine Chemicals: Lagging in Intelligentization Initiation, Only Achieving Basic Monitoring Upgrades
SMEs in fine chemicals have weak financial strength, insufficient technological reserves, and a shortage of talent. They are the "lagging group" in sensor intelligentization upgrades. Currently, their intelligentization level is still in the "primary stage," only able to achieve basic real-time monitoring, unable to realize high-end functions like data optimization and remote maintenance.
(1) Sensor networking rate: Only 20%-35%. Most sensors still operate independently, with data manually recorded or read by standalone machines, failing to interface with MES systems.
(2) Intelligent applications: Mainly traditional monitoring, with very rare applications of AI algorithms or cloud platform technologies. Only some enterprises, due to customer requirements, are equipped with basic data traceability systems, and the system functions are single, only able to achieve data storage without analysis and optimization. The core functions of sensors still remain at the "over-limit alarm" level.
(3) Data utilization rate: Less than 5%. Data collected by sensors is only used for post-event traceability and over-limit investigation, not combined with production process optimization.
(4) Financial and technological investment: Annual intelligent transformation investment is less than 5 million RMB. Lacking professional IoT and AI technology teams, core technologies rely on external suppliers. Restricted by funds, it is difficult to bear the procurement costs of high-end intelligent sensors and system integration costs.
Core Bottlenecks in Intelligentization Development
1. Technological Bottleneck: Insufficient Adaptability, Integration, and Stability
(1) Insufficient multi-medium adaptability: The production media in fine chemicals are complex. A single production line may switch between multiple strong acid, strong alkali, and organic solvent media. Existing intelligent sensors are mostly "single-medium adapted" and difficult to adapt to multiple media simultaneously.
(2) Shallow integration of algorithms and processes: Current AI algorithms are mostly general models, not custom-trained for the special processes of different fine chemical sub-sectors, resulting in poor algorithm optimization effects.
(3) Contradiction between low power consumption and stability: Sensors need to work in high-temperature, high-humidity, and strong-corrosion environments for a long time. Low-power design easily leads to unstable signal transmission, causing data loss and delay; while improving signal stability increases sensor power consumption, requiring frequent battery replacement and increasing maintenance costs.
2. Cost Bottleneck: High Procurement, Integration, and O&M Costs
(1) High procurement costs for high-end sensors: The unit prices of AI-integrated sensors and new material sensors far exceed those of traditional sensors. The unit price of SiC pH sensors is about 15,000 RMB, 1.8 times that of traditional glass electrode sensors; the unit price of quartz laser particle sensors is about 150,000 RMB, and after supporting AI algorithm modules, the unit price increases to 180,000 RMB.
(2) High system integration costs: Integrating sensor networking with MES systems and cloud platforms requires customized development, including hardware modification, software adaptation, and data interface debugging. The integration cost for a single production line is about 2-5 million RMB. For SMEs, the annual profit of a single production line is about 8 million RMB, and the integration cost accounts for 37.5%. The low input-output ratio leads to a weak willingness for intelligent transformation among enterprises.
(3) High O&M costs: Intelligent sensors require regular software upgrades and algorithm optimization. After core components are damaged, maintenance costs are high, 2-3 times that of traditional sensor maintenance.
3. Standards and Ecosystem Bottlenecks: Non-uniformity, Insecurity, and Lack of Collaboration
(1) Non-uniform industry standards: Communication protocols, data formats, and technical parameter labels of different sensor suppliers are not uniform, making it difficult for multi-brand sensors to achieve interconnection.
(2) Lack of data security standards: The production process data collected by sensors is the core secret of fine chemical enterprises, including reaction parameters, purification processes, and product formulas. However, the industry currently lacks security standards for sensor data transmission, storage, and usage.
(3) Insufficient industrial chain collaboration: There is a lack of deep collaboration among sensor suppliers, algorithm developers, fine chemical enterprises, and university research institutes.
4. Talent Bottleneck: Shortage of Composite Talents and Insufficient Attractiveness to SMEs
(1) Shortage of composite talents: Practitioners in China's fine chemical industry are mostly skilled in process control and lack IoT and AI technology reserves; while practitioners in the IoT and AI industries are mostly skilled in technological R&D and do not understand the production processes and sensor application scenarios of fine chemicals. The disconnect between the two leads to a shortage of composite talents. Large enterprises need to spend high costs to introduce them externally, while SMEs find it difficult to attract them.
(2) Insufficient talent attractiveness for SMEs: Due to limitations in financial strength, development space, and compensation, SMEs find it difficult to attract high-end composite talents, and core technologies rely on external suppliers. When intelligent sensors have data anomalies or faults, enterprises cannot troubleshoot and solve them in time, and can only wait for supplier technicians to come on-site, leading to extended production line downtime and increased downtime losses.
(3) Imperfect talent training system: Current talent training in universities is mostly single-major training, with few composite professional courses like "fine chemicals + IoT" or "fine chemicals + AI." Talent training is disconnected from industry needs. Meanwhile, internal enterprise training mostly focuses on process control or basic sensor maintenance, lacking systematic training for intelligent technologies, making it difficult for employees' technical levels to adapt to the needs of intelligent upgrades.
VI. Breakthrough Paths and Development Recommendations
Technological Breakthrough Paths
1. R&D of Multi-Medium Adapted Intelligent Sensors
(1) Adopting composite coating technology: Coating multiple layers of composite coatings (e.g., PTFE + ceramic coating) on the surface of sensor sensitive elements to enhance the sensor's ability to resist strong acids, strong alkalis, and organic solvents, achieving multi-medium adaptation. For example, developing pH sensors with composite coatings that can be used in both the strong acid environment of agrochemicals and the organic solvent environment of pharmaceutical intermediates, extending the service life to over 3 years.
(2) Modular design: Adopting a modular structure to replace corresponding sensing modules for different medium environments without replacing the entire sensor. For example, designing modular corrosion-resistant pressure transmitters, replacing corresponding diaphragm modules according to different media (Hastelloy modules for strong acid environments, SiC modules for organic solvent environments), reducing usage costs by over 40%.
2. Promoting Deep Integration of Algorithms and Processes
(1) Establishing an industry process database: Led by industry associations, uniting fine chemical enterprises, universities, and research institutes to establish process databases for different sub-sectors, covering core information such as reaction parameters, medium characteristics, and sensor data, providing precise data support for AI algorithm training. For example, establishing a process database for the synthesis reaction of pharmaceutical intermediates, including data on reaction rates and product yields under different temperatures and pH values, to improve the optimization accuracy of AI models.
(2) Customized algorithm development: Developing customized AI algorithms for the special processes of different sub-sectors like pharmaceutical intermediates and electronic chemicals. For example, developing a particle data compensation algorithm considering high-purity characteristics for electronic chemicals, and a viscosity optimization algorithm adapting to the rheological characteristics of different coatings for the coatings industry, improving algorithm optimization effects by 10%-15%.
3. Optimizing Low-Power and Stability Design
(1) Adopting new low-power chips: R&D of low-power MEMS sensor chips adapted to harsh environments to reduce sensor power consumption while improving signal transmission stability. For example, developing low-power chips based on SiC MEMS technology to extend the battery life of wireless sensors to over 1 year and reduce the signal transmission packet loss rate to below 5%.
(2) Application of wireless charging technology: Deploying wireless charging equipment in production workshops to provide continuous power supply for IoT sensors. For example, deploying wireless charging coils around reactors in agrochemical plants allows sensors to charge in real time without battery replacement, completely solving the contradiction between low power consumption and stability.
Cost Control Strategies
1. Scaled Cost Reduction
(1) Promoting standardized production of sensors: Led by relevant national departments, formulate intelligent sensor industry standards, unify communication protocols, data formats, and core component specifications to achieve scaled production. For example, unifying the electrode size and communication interface of pH sensors can reduce the procurement cost of core sensor components by 15%-20%.
(2) Industrial chain collaborative cost reduction: Sensor suppliers sign long-term cooperation agreements with fine chemical enterprises to reduce procurement costs through bulk purchasing; suppliers collaborate with raw material enterprises to optimize raw material procurement channels. For example, SiC material suppliers cooperate with sensor enterprises to reduce SiC sensor costs by 30% through bulk purchasing of SiC wafers.
2. Phased Intelligent Transformation
(1) SMEs prioritize transforming core links: SMEs can first upgrade the sensors in core production links (e.g., reactors, purification processes) to intelligence, and then gradually expand to other links. For example, a small pharmaceutical intermediate plant first intelligently transforms the temperature and pH sensors of the cephalosporin intermediate synthesis reactor, investing about 800,000 RMB to achieve precise control of core process parameters and increase product yield by 2%, before gradually upgrading sensors in other links.
(2) Shared intelligent platforms: SMEs in the same region can jointly build a shared sensor cloud platform to share platform construction and O&M costs. For example, 10 small coating enterprises in a chemical park jointly build a shared cloud platform. The platform construction cost is shared by the 10 enterprises, reducing the investment cost per enterprise by 90%.
Recommendations for Standards and Ecosystem Construction
1. Improving the industry standard system to align with future development directions and promote the development requirements of unified standards and interconnection in high-end manufacturing:
(1) Formulating intelligent sensor technical standards: Clarify technical parameters, performance requirements, and testing methods for intelligent sensors in different sub-sectors. For example, formulating metal ion precipitation limit standards for sensors used in electronic chemicals (≤0.1ppb) and corrosion resistance rating standards for sensors used in agrochemicals (≥IP68).
(2) Unifying data communication and security standards: Formulate sensor data communication protocol standards (e.g., adopting LoRaWAN industry-specific protocols) and data security standards, requiring encrypted technology for sensor data transmission and permission management mode for storage to ensure data security.
2. Building a Collaborative Development Ecosystem
(1) Establishing an industry-university-research-application cooperation platform: Led by the government, uniting sensor enterprises, algorithm developers, fine chemical enterprises, and university research institutes to establish a cooperation platform for joint research on key technologies. For example, establishing a "Fine Chemical Intelligent Sensor Innovation Alliance" focusing on core technical challenges like multi-medium adaptation and algorithm optimization to jointly develop solutions.
(2) Cultivating third-party service institutions: Cultivate professional third-party technical service institutions to provide SMEs with one-stop services such as sensor selection, system integration, and O&M. For example, third-party institutions provide intelligent sensor selection advice for small electronic chemical plants and are responsible for system integration and later O&M, so enterprises do not need to equip professional technical teams.
Talent training is the core requirement for strengthening the upgrading of high-end manufacturing. Specific recommendations are as follows:
1. University Talent Training
(1) Opening composite professional courses: Offer composite professional courses like "fine chemicals + IoT" and "fine chemicals + AI" in universities to cultivate composite talents who understand both processes and technologies. For example, East China University of Science and Technology offers a "Intelligent Sensing and Fine Chemicals" professional direction, with courses covering chemical processes, sensor principles, and machine learning.
(2) University-enterprise cooperative training: Universities cooperate with fine chemical enterprises and sensor enterprises to establish training bases, allowing students to participate in actual intelligent transformation projects. For example, a university cooperates with Wanhua Chemical to establish a training base, where students participate in the sensor networking project of Wanhua Chemical's fine chemical sector to accumulate practical project experience.
2. Enterprise Talent Training
(1) Internal training: Enterprises regularly organize employees to participate in IoT and AI technology training to improve the technical level of existing employees. For example, a large agrochemical enterprise organizes production backbones to participate in intelligent sensor O&M training every month, enabling employees to independently solve common sensor faults.
(2) External introduction and incentives: Enterprises introduce preferential policies to attract high-end composite talents, such as providing equity incentives and research funding support; reward internal technological innovation personnel to stimulate employees' innovation enthusiasm. For example, an enterprise rewards the team that developed the multi-medium adapted sensor with 500,000 RMB.
Conclusion
Currently, although the intelligent development of fine chemical sensors faces multiple bottlenecks such as technology, cost, standards, and talent, under the guidance of the "15th Five-Year Plan" policies for high-end, intelligent, and green development, breakthroughs are expected through measures in technological innovation, cost control, standard construction, and talent training.
In the future, with the acceleration of localized development of high-end intelligent sensors, the improvement of the industry standard system, and the construction of a collaborative ecosystem of industry-university-research-application, intelligent sensors will achieve large-scale popularization in the fine chemical industry, driving the industry to reduce costs, increase efficiency, and upgrade to high-end, providing strong support for the high-quality development of the petrochemical industry. Meanwhile, the fine chemical sensor market will maintain a rapid growth trend. High-end sensors in the fields of pharmaceutical intermediates and electronic chemicals will become the core engine of market growth, bringing broad development space for sensor enterprises.
Author | Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Jiusan Society Central Committee and Executive Vice Chairman of the China Sensor and IoT Industry Alliance
Editor | Yang Pengyue Art Editor | Malia Supervisor | Zhao Chen