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Digitalization in the Energy Sector
Published in Muhammad Asif, Handbook of Energy Transitions, 2023
Muhammad Umer, Muhammad Abid, Tahira Nazir, Zaineb Abid
The adaptation of digitalization in the energy sector has been classified into three broader domains, that is, system balance, process optimization, and customer orientation: Firstly, the system balance comprises the processes that enable maintaining energy generation levels in alignment with the end-user demand. Secondly, process optimization aims to continually improve the internal process to enhance the efficiency and effectiveness of the overall systems. Thirdly, the customer orientation focuses on creating value addition for the consumer and generating increased revenues. The aforementioned three domains have been subcategorized into seven aspects that make up the latest technologies being used in the energy sector (Alekseev, Lobova, Bogoviz, & Ragulina, 2019; Parida et al., 2019).
Modular Process Simulators
Published in Mariano Martín Martín, Introduction to Software for Chemical Engineers, 2019
Rubén Ruiz-Femenía, César Ramírez-Márquez, Luis G. Hernández-Pérez, José A. Caballero, Mariano Martín, José M. Ponce Ortega, Juan Gabriel Segovia
Process optimization consists of finding the best operating conditions, the selection of the best technology or the best configuration of the process flowsheet. General search and optimization techniques are classified into three categories: enumerative, deterministic, and stochastic [51].
Optimization of cooling condition and energy parameters during laser bending of Duplex-2205
Published in Materials and Manufacturing Processes, 2023
Process optimization plays a vital role in manufacturing industries, aiming to improve operational efficiency, reduce costs, and enhance product quality. Traditional optimization techniques such as gradient-based methods,[21] linear programming,[22] particle swarm optimization,[23] Monte Carlo methods, and sequential quadratic programming[24] have been widely utilized in manufacturing process optimization. Moreover, recent advancements in the field have led to the exploration of new and efficient techniques such as genetic algorithms,[25] multiobjective optimization[26,27] (e.g., Pareto-based methods,[28] weighted sum method), fuzzy logic,[29,30] surrogate modeling,[31] neural networks,[32] and data-driven methods.[33,34] These techniques enable researchers and practitioners to effectively address complex optimization problems and achieve optimal process configurations.
Kinetic modeling and statistical optimization of submerged production of anti-Parkinson’s prodrug L-DOPA by Pseudomonas fluorescens
Published in Preparative Biochemistry & Biotechnology, 2022
Ananya Naha, Santosh Kumar Jha, Hare Ram Singh, Muthu Kumar Sampath
Taguchi orthogonal array is an effective and robust design methodology for optimizing bioprocess for any molecule production involving multiple factors and significant interactions between the factors. Along with process optimization, it has added advantage of process cost reduction.[40]Figure 3 shows the L-DOPA production in designed experimental conditions under submerged fermentation by P. fluorescens. The production of L-DOPA from all the different 9 trial conditions was used to identify the optimum levels of each factors, S/N ratio and to predict the other parameters like severity index.[14] The matrix combination of trial-6 consisting lactose-2 g/l, tryptone-4 g/l, NaCl-1.2 g/l, and L-tyrosine-0.1g/l having a maximum yield of L-DOPA (3.136 ± 0.06 g/l), while the least yield of 1.538 ± 0.025 g/l was reported in trial matrix-3 having lactose-2 g/l, tryptone-6 g/l, NaCl-1 g/l and L-tyrosine-0.15 g/l.
Multi-objective optimization through a novel Bayesian approach for industrial manufacturing of Polyvinyl Acetate
Published in Materials and Manufacturing Processes, 2023
Arjun Manoj, Srinivas Soumitri Miriyala, Kishalay Mitra
Process optimization is key for improving operating conditions, control loops, equipment, resources, and labor working conditions in manufacturing industries, eventually resulting in maximum productivity. Algorithms belonging to classical[1] and evolutionary[2,3] paradigms have been developed in the past to solve the well-documented optimization formulations; however, with every real-world applications in manufacturing industries, different challenges arise that need in-depth study and tailormade strategies. One such challenge is when the objective and constraint evaluation is time-expensive.[4]