Profit Maximization Techniques for Operating Chemical Plants. Sandip K. LahiriЧитать онлайн книгу.
decrease capacity in tune with global demand. For that companies need to have enough extra cash in hand so that they can use it as a buffer to respond to global business uncertainties.
Implementation of an effective profit maximization project is the only way to generate this buffer money and strategically position the company in a better way to sustain their operation amid global business uncertainties.
1.5 Data Rich but Information Poor Status of Today's Process Industries
With the advent of faster computers in CPI, a large amount of process data is collected and stored every minute by data historian software like IP21, the Pie system, Exaquantum, etc. Every second or minute data of all process parameters of whole plants are now available. This large historical data depository is a distinct feature of today's CPI as compared to older generation plants. These real‐time process data are like an untapped gold mine. Many insights and much process knowledge can be generated from these large sets of operating data. However, very little has been done so far. Due to the unavailability of effective process data analytics, the knowledge hidden in such data could not be tapped properly. The main concerns of CPIs are how to extract meaningful information from these data. Thus, today's chemical industry remains data rich but information poor. There is a need to generate an effective framework where knowledge can be extracted from this wealth of data. Advanced AI based big data analytics systems need to be applied to extract knowledge. The capability to meet this challenge is key for business excellence.
1.6 Emergence of Knowledge‐Based Industries
The speed of technological advancement in the last 20 years makes older technologies obsolete at a speed never before achieved. In today's cut‐throat global competitive environment, companies that follow the old way of doing business gradually become obsolete and die over time. Many chemical companies of the 1980s or 1990snow no longer exist. Companies who could not adopt new technologies and new ways of doing business gradually perish. Only knowledge‐based chemical industries survive. Only CPIs employing knowledge to drive their businesses are going to survive in the future. This essentially means generating an effective platform that can generate knowledge from available business data and use this knowledge to develop a unified framework to support faster business decisions to respond to external market uncertainties. Companies who utilize this knowledge to drive their businesses are called knowledge‐based chemical industries. It is survival of the fittest scenario and only knowledge‐based industries that adapt to a changing business scenario will survive in the future. All other companies, who fail to integrate their knowledge with business, will gradually perish. This gives rise to a new generation of process industries. The emergence of these new generation process industries in this decade is the most important phenomena in CPI.
1.7 How Knowledge and Data Can Be Used to Maximize Profit
New ways of doing business are key for survival. Intelligent industries are those who can adapt quickly to this knowledge and innovation era. However, this needs a complete mindset change. How we generate useful knowledge and integrate it with business decisions is the real challenge of today's CPI. A new look to the old problems is absolutely necessary. A new way to increase equipment reliability, novel methods for process data monitoring, and a new emphasis on real‐time optimization are what is now needed. How data and knowledge can be used to maximize profit is the real key driver and all the chapters of this book are dedicated to that. Companies took multi‐faceted a completely new advanced approach to deal with this challenge. Some of the common solutions Global good companies have implemented are as follows:
Real‐time optimization (RTO) and advanced process control (APC) are implemented for real‐time optimization of plant. These tools ensure running the plant with simultaneous multiple constraints.
Implementation of an advanced artificial intelligence (AI) base, online data monitoring, and fault diagnosis detect any abnormality of process equipment at its incipient stage.
All these plants run 25–50% more capacity than their design capacity.
Online equipment reliability monitoring systems are implemented.
Risk‐based inspection systems are in place.
Online supply chain management system.
SAP‐based reliability centered maintenance practice.
A management framework to encourage people participation and to tap their ideas for small improvements in the plant.
References
1 Lahiri, S.K. (2017a). Front matter. In Multivariable Predictive Control (pp. i–xxxiii). https://doi.org/10.1002/9781119243434.fmatter.
2 Lahiri, S.K. (2017b). Introduction of model predictive control. In Multivariable Predictive Control (pp. 1–21). https://doi.org/10.1002/9781119243434.ch1.
2 Big Picture of the Modern Chemical Industry
2.1 New Era of the Chemical Industry
Since 1746, evolution of the modern chemical industry can be divided into four distinct stages of development: the industrialization era (chemical industry 1.0), the scale and capacity building era (chemical industry 2.0), and the automation and computerization era (chemical industry 3.0). Currently the chemical industry is slowly entering into a new era called data analytics and the artificial intelligence (AI) stage (4.0). Disruptive technologies like artificial intelligence, machine learning, big data analytics, and the internet of things (IoT) have already entered inside the chemical process industries and are already changing the rule of the chemical business (Ji, He, Xu, and Guo, 2016). Their influence is starting to see benefits in a significant improvement in production efficiency, energy utilization, optimization of the entire manufacturing process, integration of the supply chain, new product development, product delivery speed, etc. Figure 2.1 shows the development stages of the chemical industry.
Figure 2.1 Developing stages of the chemical industry
2.2 Transition from a Conventional to an Intelligent Chemical Industry
The recent advances of these disruptive digital technologies give birth to a new generation of intelligent chemical industries (Ji et al., 2016). The old method of doing business by the conventional chemical industry are slowly becoming obsolete. Distinct features of a new generation of intelligent chemical industries are given below (but are not limited to these):
A new generation of intelligent chemical industries use data analytics to take informed decisions in every phase of business, be it manufacturing, marketing, or R&D (research and development). These intelligent chemical industries develop a complete infrastructure of digital platforms to collect and analyze data and integrate it with business processes. This is called digital transformation.
They generate knowledge from the available data by using artificial intelligence‐based algorithms. This knowledge is used to integrate shareholder value, market demands, and sustainable development.
Manufacturing facilities of these new generation of chemical industries are transformed from island mode to integrated mode. Operation of the supply chain, manufacturing facility, marketing, and R&D are integrated to leverage a larger optimization scope.
The process control of these process industries is not