The chemical industry occupies a central place in the global economy, playing a key role in providing society with necessary goods and raw materials. Its products are used in almost all spheres of life, whether it is household goods, building materials, pharmaceuticals or fuel. With the constant increase in demand for high-quality and safe products, chemical industry enterprises are forced to look for ways to increase their production efficiency. One of these features is chemical sensors, which provide a unique opportunity to monitor and control production processes in real time. These devices allow not only to improve the quality of products, but also to reduce operating costs, which makes them strategically important for the sustainable development of the industry.

Artificial intelligence (AI) makes a special contribution to the modernization of chemical sensors, which takes their functionality to a new level. Using machine learning and deep learning techniques, AI is able to analyze huge amounts of data coming from sensors and turn them into useful information for making managerial decisions. This opens up new horizons for the creation of smart monitoring systems that can significantly improve the efficiency and safety of production processes in the chemical industry.

This article is devoted to the study of economic prospects and technological trends related to the introduction of chemical sensors based on AI in the chemical industry. This study will examine current trends in digital technology, the impact of AI on the development and operation of sensors, as well as real-world examples of successful applications of these technologies. In addition, special attention will be paid to key economic indicators and potential barriers that could slow down the mass adoption of innovative solutions.

The modern chemical industry is on the verge of significant changes due to the rapid development of digital technologies. Among the key trends determining the vector of the industry’s development, digital transformation, automation of production processes and the active introduction of the Internet of Things (IoT) stand out.

Digital transformation implies the transition from traditional analog control systems to modern digital platforms that allow collecting, storing and analyzing large amounts of data. This makes it possible to better understand production processes, anticipate possible failures, and optimize equipment performance. Platforms such as SAP S/4HANA or Microsoft Azure are already successfully used in the chemical industry for supply chain management, production planning and quality control.

Automation of production processes is becoming an integral part of the strategy of most chemical industry enterprises. Robots, automated production lines and process control systems (APCs) can significantly increase labor productivity, reduce the impact of the human factor and reduce the number of errors. For example, robotic manipulators are used to perform monotonous and dangerous operations such as loading and unloading reagents, which increases safety in production [1].

The Internet of Things is also having a significant impact on the chemical industry. Devices connected to the network collect real-time data and transmit it to centralized servers for analysis. This allows you to instantly respond to changes in production processes, prevent equipment breakdowns and optimize resource consumption. For example, pressure, temperature and liquid level sensors can be integrated into a single monitoring system, which ensures more precise process control and minimizes the risks of emergencies.

Artificial intelligence (AI) has a significant impact on the development and improvement of chemical sensors, opening up new opportunities to improve their efficiency and accuracy. One of the key aspects of using AI is the automation of data analysis processes, which allows faster and more accurate interpretation of information received from sensors. Let’s look at the main approaches and methods used in this field.

Machine learning (ML) is a powerful tool for analyzing huge amounts of data coming from chemical sensors. ML algorithms are able to identify hidden patterns and correlations that may be invisible to the human eye. For example, clustering and classification methods can be used to segment data by various parameters, such as mixture composition, temperature, or pressure, and predict the behavior of the system under various conditions [2].

ML also allows you to create predictive models that can warn about possible failures or changes in the production process long before they occur. This is especially valuable in the chemical industry, where timely detection of deviations can prevent serious accidents and losses.

Deep learning (DL) is a subset of machine learning that specializes in processing complex and multidimensional data. DL models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to analyze images, time series, and other types of data specific to the chemical industry.

For example, CNNs can analyze images taken from surveillance cameras and identify deviations in the color or shape of a product, which may indicate problems in the manufacturing process. RNNs, in turn, are ideal for analyzing time series such as temperature or pressure data, and can predict future events based on historical data.

Specialized software platforms and libraries play an important role in the successful implementation of AI in sensor development. For example, tools such as TensorFlow, PyTorch, and Scikit-learn provide developers with user-friendly interfaces for creating and testing ML and DL models. Cloud computing platforms such as AWS and Google Cloud Platform provide the necessary infrastructure for storing and processing big data [3].

These tools allow engineers and scientists to quickly develop and test new models, as well as integrate them into existing production systems. This accelerates the innovation process and reduces research and development costs.

One example of successful AI applications in the chemical industry is the use of neural networks to monitor the condition of catalysts in oil refineries. Catalysts play a key role in oil refining processes, and their wear can lead to significant losses. With the help of AI, it is possible to predict the service life of a catalyst and replace it in a timely manner, avoiding unplanned production shutdowns [4].

Another example is the use of AI to analyze the composition of atmospheric emissions. Chemical sensors equipped with AI can monitor pollution levels in real time and warn of excess levels, which helps companies comply with environmental standards and avoid fines.

According to an analytical report by McKinsey, the introduction of artificial intelligence-based sensors in the chemical industry can lead to a 10-20% increase in productivity by optimizing production processes and reducing losses. For example, sensors installed on pipelines can automatically regulate the flow of liquid, which reduces electricity and water consumption by 15%.

Rosstat data confirms that the Russian chemical industry is growing by 3-5% annually, and the introduction of innovative technologies such as AI sensors can further stimulate this growth. According to experts, resource savings can amount to up to 300 million rubles per year for a medium-sized enterprise [5].

Product quality is a key factor of competitiveness in the international market. AI-based sensors allow real-time monitoring of production process parameters, which helps to avoid defects and non-compliance with standards. For example, the pH level sensor in the reactor can automatically adjust the dosage of the reagent, maintaining the optimal reaction mode.

According to industry associations, the use of AI sensors in the European chemical industry has led to a 12% reduction in the percentage of defective products. Similar results are expected in Russia, which will increase the export potential of domestic chemical products.

Analysis of Rosstat data shows that the cost of energy resources and raw materials accounts for a significant share in the cost of chemical products. The introduction of AI sensors makes it possible to optimize the use of these resources, which leads to a 5-10% reduction in cost.

An example from practice: at one of the Russian polymer production plants, the installation of temperature and pressure monitoring sensors led to an 18% reduction in energy consumption and a 9% reduction in raw material costs. This allowed the company to increase profitability by 7%.

Innovative technologies open up new business opportunities. For example, AI-based sensors can be used to create remote monitoring and diagnostic services for equipment. This is especially true for remote regions where maintenance of equipment is difficult.

According to analysts, the market for AI sensor-based services in the chemical industry will grow by 35% in the next five years. Russian companies can take a leading position in this market by offering competitive solutions.

The introduction of chemical sensors based on artificial intelligence in the chemical industry represents a significant step forward, opening up new horizons for increasing efficiency, reducing costs and improving product quality. The analysis showed that the use of such technologies can have a positive impact on various aspects of the production process [6].

Statistics show that the introduction of AI sensors can lead to a 10-20% increase in productivity, a 5-10% reduction in production costs, and a 4-5% improvement in quality. In addition, the market for AI sensor-based services is projected to grow by 35% in the next five years, which confirms the high economic prospects of this technology.

It is important to note that the successful implementation of AI sensors requires significant investments in infrastructure and training. However, according to calculations, the payback period for such investments is 2-3 years, which makes them attractive for the long-term development of enterprises.

The introduction of chemical sensors based on artificial intelligence in the chemical industry opens up broad prospects for economic growth and technological progress. These technologies can significantly improve the efficiency of production processes, improve product quality and reduce costs, making them strategically important for the sustainable development of the industry.

One of the key advantages of using AI sensors is increased productivity. By fine-tuning and optimizing production processes, enterprises can reduce equipment downtime, reduce scrap rates, and increase the yield of high-quality products. This, in turn, leads to increased profits and competitiveness in the global market. In addition, AI sensors contribute to significant resource savings. They make it possible to optimize the use of raw materials, energy and water, which reduces the cost of production and reduces the burden on the environment. For example, temperature and pressure monitoring sensors can reduce energy consumption by 15-20%, resulting in significant cost savings. It is also worth noting that the introduction of AI sensors creates new market niches and services. For example, remote monitoring and equipment diagnostics services are becoming in demand, especially in hard-to-reach regions. This opens up additional sources of income and strengthens the companies’ positions in the market [7].

The potential of using AI in the chemical industry is far from exhausted. In the future, we can expect closer integration of AI with quantum computing, which will significantly accelerate the modeling and prediction of chemical processes. This will pave the way for the creation of even more accurate and reliable sensors capable of operating in extreme conditions. In addition, the development of AI will lead to the emergence of more intelligent production management systems that will be able to independently make decisions based on real-time data analysis. This will create the prerequisites for the transition to fully autonomous production, where the human factor will be minimized.

Thus, the introduction of chemical sensors based on artificial intelligence represents an important step towards the digital transformation of the chemical industry. These technologies not only increase the economic efficiency of enterprises, but also contribute to solving global environmental problems. Investments in AI sensors today lay the foundation for the sustainable development of the industry tomorrow, ensuring its competitiveness and technological leadership for many years to come.