derivative) control but expands to advance process control and real‐time optimization covering the production process, entire marketing, and supply chain operation.
With the help of data analytics and artificial intelligence‐based algorithms these chemical industries develop a knowledge‐based decision‐making capability in every aspect of business and make themselves better prepared to handle more stringent environmental requirements and changing customer needs.
The comparisons between an intelligent chemical industry and the conventional chemical industry are listed in Table 2.1 (Ji et al., 2016).
2.3 How Will Digital Affect the Chemical Industry and Where Can the Biggest Impact Be Expected?
The global management firm M/s Mckinsey has studied and reported that digital transformation is changing the entire chemical business in three major ways, as depicted in Figure 2.2 (Klei et al., 2017).
Table 2.1 Comparisons between smart and conventional chemical industries
| Items | Conventional chemical industry | Intelligent chemical industry |
| Integration mode | Integration for processes | Integration of supply chain network |
| Optimization goals | Profit optimization on specific conditions | Profits optimization considering market demand, device status, energy conservation and emissions reduction |
| Optimization patterns | Serial mode conducted offline | Synchronous optimization of decision‐making and control adjustment employed online |
| Technical economic feature | Large‐scale | Equilibrium between large‐scale and necessary flexibility |
| Operation mode | Specialized manufacturing | Combination of manufacturing and service |
| Decision factors | Operational and technical factors | Users' requirements, products, quality standard, operating condition, resource, system reliability status |
| Control mode | Discrete control | Advanced process control |
| Intelligent degree | Low level | Artificial intelligence embedded in the process optimization control |
| Control platform | Discrete control system | Contemporary integrated process system |
| Flexibility | Limited flexibility, adaptive scope and function redundancy | More flexible configuration, adaptive to multiple optimization control modes |
| Data supporting | Local small data | Big data |
| Algorithm | Traditional statistical analysis | Statistical analysis, data mining, AI and visualization techniques |
Figure 2.2 Three major ways digital transformation will impact the chemical industry
2.3.1 Attaining a New Level of Functional Excellence
Data analytics and AI‐based interpretation is helping efficiency improvement of all core business processes of the chemical industry, including manufacturing, marketing and sales, and R&D. Data‐based decision making, called digital in short, provides the means to unlock a new level of productivity enhancement (Klei et al., 2017).
2.3.1.1 Manufacturing
Manufacturing operations consume most of the production costs and digital technology can bring the highest impact in this area. This is true for all segments of the chemical industry, from petroleum refinery to petrochemicals to pesticides to specialty chemicals. The global management firm Mckinsey estimate the potential for a three‐ to five‐percentage point improvement in return on sales from employing digital in production operations (Klei et al., 2017). With the advent of data historians two decades ago, an enormous amount of production‐related data has been collected by all of the major chemical industries. However, due to the absence of proper data analytics software, most of these data remains unutilized. AI‐based algorithms can extract knowledge from these data and utilize that knowledge to achieve higher efficiency and throughput, lower energy consumption, and more effective maintenance. For many companies, these are low hanging fruits and benefit can be achieved immediately using existing IT (information technology) and process control systems.
The contribution to profits can be substantial. Examples are many in all leading chemical industries around the world. A major petrochemical company applied advanced AI‐based data analytics to a billion data points that it collected from its naphtha cracker manufacturing plant. With the help of an AI‐based stochastic optimization algorithm, this plant optimizes different process parameters that lead to an increase in the ethylene production by 5% without making any capital investments and generated cost savings by reducing energy consumption by 15%. A leading refinery company takes another approach at one of its main plants: it used AI‐based advanced analytics to model its production process and make a virtual plant, and then used the model to provide detailed, real‐time guidance to DCS (distributed control system) panel operators on how to adjust process parameters to optimize performance. Once it was implemented, profit from this plant increased by over 25% and yields increased by seven percentage points, thus saving on raw materials, while energy consumption fell by 26% (Holger Hürtgen, 2018).
Besides this AI‐driven analytics‐based opportunity, there are other digital‐enabled advances that have started creating profit in the manufacturing operations area. Examples include IoT‐based steam trap monitoring, IoT‐based wireless vibration and temperature monitoring of critical pumps and other single‐line rotating equipment, the use of digital sensors to monitor vent gas composition, etc. These advances help to reduce maintenance costs and improve process reliability and safety performance. At the same time, deploying a holistic automated and centralized data analytics and plant performance management system should enable the plant engineers to monitor the plant better and take proper corrective and preventive actions faster.
2.3.1.2 Supply Chain
Digital technology also can bring enormous value to the entire supply chain, including inbound and outbound logistics and warehousing. From past historical data, an intelligent algorithm can significantly improve accuracy of forecasting, which helps to optimize the entire sales and operations planning process (Klei et al., 2017). Digital technology can be used to leverage better scheduling of batch production, shorter lead times, and lower safety stocks with a higher level of flexibility. A digital enabled holistic system can be built to develop integrated “no touch” ordering and scheduling systems.