Artificial intelligence in nonlinear process control based on fuzzy logic

Shteimberg, E.; Kravits, M.; Ellenbogen, Amir; Arad, M.; Kadmon, Y.

doi:10.1109/eeei.2012.6376916

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…The parameters of the fuzzy PID method are consistent with the relevant parameters of the fuzzy part of the proposed control method [9] . However, due to unpredictable load changes, we should ignore the load when calculating load correlation d (Here is to simplify the design and choose to ignore, in fact, other feasible way is to build additional observers to estimate it) [10] .…”

Section: Simulink Verificationmentioning

confidence: 70%

Fuzzy Sliding Mode Variable Structure Control for Brushless DC Motor Position Servo System

Zhang¹,

Tang²,

Di-fei³

et al. 2017

dtetr

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Abstract.To solve the problem of the chattering and strict requirement of the accurate parameter of the model in the sliding mode control, a fuzzy sliding mode controller is proposed in this paper. The new model is constructed by means of introducing fuzzy control into sliding mode control. Parameters of PID control method and reaching law are real-time updated by using fuzzy rule inference. The proposed method utilizes full advantages of fuzzy PID control and sliding mode control so that the position tracking performance and robustness of the system are remarkably improved. In addition, chattering of sliding mode control is reduced. Experiments results are presented to validate the correctness and effectiveness of the proposed method.

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Section: Simulink Verificationmentioning

confidence: 70%

Fuzzy Sliding Mode Variable Structure Control for Brushless DC Motor Position Servo System

Zhang¹,

Tang²,

Di-fei³

et al. 2017

dtetr

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“…Fuzzy logic is a mathematical framework that can handle imprecise and uncertain information. Fuzzy logic control can be effective for nonlinear processes that exhibit fuzzy or uncertain behavior 109, 110. A fuzzy‐based controller is established on three elements: a fuzzification interface, a rule‐base (a set of If‐Then rules), and performance indices 111 as shown in Fig.…”

Section: Control Theorymentioning

confidence: 99%

State‐of‐the‐Art Review on the Applications of Nonlinear and Artificial Intelligence‐Based Controllers in Petrochemical Processes

Ansari

Taqvi

2023

ChemBioEng Reviews

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Petrochemical industries are facing severe challenges in controlling nonlinear processes which are to be analyzed and monitored to develop efficient control models for improved efficiency and productivity. This review focuses on the control theory of petrochemical processes along with linear and nonlinear controllers with their limitations as well as on the application of artificial intelligence (AI)‐based controllers in multiple sectors of petrochemical industries. The findings and results from the review prove that the AI control technique is a promising one in the future for uncertain dynamic systems. The paper also covers a review of the vinyl chloride monomer (VCM) process and a recent control study on the plant. Being a nonlinear system, VCM requires advanced control techniques to control the uncertain dynamics of the process. The study shows a gap in the development of control strategies applied to VCM in terms of sustainability, efficiency, and environment‐concerned issues. Valuable insights for researchers, practitioners, and engineers engaged in process control and automation in the petrochemical industry are provided.

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“…AI-based methods offer an alternative to traditional approaches when it comes to modeling and controlling complex systems (Shteimberg et al, 2012). Artificial intelligence (AI) is a specialized field within computer science that addresses problems traditionally believed to require human intelligence for their solution (Shteimberg et al, 2012). Within this field, there exist methods that can either support or completely replace human involvement in solving intricate problems.…”

Section: Introductionmentioning

confidence: 99%

<strong></strong>Revolutionizing Small-Scale LNG Business: Optimal Strategies for an Adaptive and Sustainable Supply Chain<strong> </strong>

JOHNSON,

Anyanwu

2023

Preprint

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Transforming Small-Scale LNG Business, Enhancing Efficiency and Resilience in an Evolving Supply Chain In various industries, innovative systems have proven their value due to their efficiency and adaptability. A prime example of this lies in the rapid growth of the liquefied natural gas (LNG) market. LNG, a fuel derived from natural gas, has gained significant traction in recent years. In light of this, we aim to explore optimal strategies for small-scale LNG businesses, with a focus on revolutionizing their supply chain to ensure adaptability and sustainability. Our objective is to enhance efficiency and sturdiness in this evolving sector. By devising innovative methods and implementing cutting-edge technologies, we aim to revolutionize the small-scale LNG market, paving the way for a more optimized and resilient supply chain. Through this transformation, we strive to create a sustainable and adaptable framework that will drive the growth and success of a small-scale LNG businesses.LNG' ability to be transported and stored more cost-effectively in its reduced volume compared to natural gas in its original state is a significant advantage. The distribution network for LNG starts at numerous supply terminals and extends to a diverse range of consumption centers, each with varying tank capacities. In the broader economic context, the establishment of efficient routes for LNG Truck tank plays a critical role.To tackle the challenge of selecting a pickup location and determining optimal destinations with specific unloading volumes for liquefied natural gas (LNG), this research presents an innovative solution utilizing state-of-the-art machine learning techniques. The proposed approach combines the power of reinforcement learning, recurrent neural networks, online learning, and graph theory. By leveraging these cutting-edge techniques, we aim to develop an intelligent system that can effectively determine the most efficient pickup location and sequence of destinations for LNG delivery. This system will take into account various factors such as transportation costs tank capacity, and demand fluctuations. Through the integration of reinforcement learning, the system will continuously learn and improve its decision-making process in real-time. The recurrent neural networks will allow for the analysis of historical aim to model and optimize the complex network of pickup and delivery routes, considering factors such as distance, capacity constraints, and operational efficiencies. With this innovative approach, we strive to revolutionize the selection and planning process for LNG pickup and delivery, ultimately enhancing efficiency and reducing cost within the LNG supply chain.Improving the distribution network offers several financial benefits, including cost reduction, faster delivery times, more affordable distribution, and enhanced efficiency in the utilization of tanker trucks for LNG transportation. By optimizing the distribution network, transit costs associated with LNG transportation can be significantly lowered. This can be achieved through intelligent route planning, taking into account factors such as distance, traffic conditions, and fuel consumption. Minimizing transit costs contributes to overall savings for businesses operating in the LNG market. Faster delivery times are another advantage of an enhanced distribution network. By designing efficient routes and eliminating unnecessary detours or delays, the time taken for LNG to reach its intended destinations can be reduced. This not only improves customer satisfaction but also allows for more frequent deliveries and better coordination of supply and demand. Cheaper distribution is a direct outcome of an optimized distribution network. By strategically planning pickup locations, destinations, and intermediate stops, the costs associated with transferring LNG between terminals and consumption centres can be minimized. This leads to savings in transportation expenses, ultimately benefiting both businesses and end-users. Efficient utilization of tanker trucks is crucial for maximizing profitability in the LNG industry. An improved distribution network enables better allocation of resources, ensuring that tanker trucks are utilized optimally. This includes reducing empty or underutilized trips, maximizing load capacities, and minimizing idle time. Such efficiency improvements translate to cost savings and increased revenue for businesses involved in LNG transportation. Overall, the financial gains from improving the distribution network are substantial. Lower transit costs, faster delivery times, cheaper distribution, and more efficient use of tanker trucks contribute to increased profitability, competitiveness, and sustainability in the LNG market.

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Artificial intelligence in nonlinear process control based on fuzzy logic

Cited by 5 publications

References 2 publications

Fuzzy Sliding Mode Variable Structure Control for Brushless DC Motor Position Servo System

Fuzzy Sliding Mode Variable Structure Control for Brushless DC Motor Position Servo System

State‐of‐the‐Art Review on the Applications of Nonlinear and Artificial Intelligence‐Based Controllers in Petrochemical Processes

<strong></strong>Revolutionizing Small-Scale LNG Business: Optimal Strategies for an Adaptive and Sustainable Supply Chain<strong> </strong>

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