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Flow measurement is an essential requirement for monitoring and controlling oil movements through pipelines and facilities. However, delivering reliably accurate measurements through certain meters requires cumbersome calculations that can be simplified by using supervised machine learning techniques exploiting optimizers. In this study, a dataset of 6292 data records with seven input variables relating to oil flow through 40 pipelines plus processing facilities in southwestern Iran is evaluated with hybrid machine-learning-optimizer models to predict a wide range of oil flow rates (Qo) through orifice plate meters. Distance-weighted K-nearest-neighbor (DWKNN) and multi-layer perceptron (MLP) algorithms are coupled with artificial-bee colony (ABC) and firefly (FF) swarm-type optimizers. The two-stage ABC-DWKNN Plus MLP-FF model achieved the highest prediction accuracy (root mean square errors = 8.70 stock-tank barrels of oil per day) for oil flow rate through the orifice plates, thereby removing dependence on unreliable empirical formulas in such flow calculations.

Although the reforming of fossil fuels to syngas followed by catalytic syngas conversion is the common route to produce methanol, concerns over climate change, growing demand for methanol, and depletion of fossil fuels have created interest to produce biomethanol from renewable feedstocks. Typically, the biomethanol production routes could be classified into two main groups based on the applied process. The first group includes the bio-syngas production from biomass resources through gasification, pyrolysis, plasma processing, and anaerobic digestion techniques, followed by bio-syngas conversion to methanol. In the second group, the biomass is biologically converted to biomethane, and the produced biomethane could be converted to methanol through conventional processes. In this chapter, the concept and reaction chemistry of biomass conversion to biomethanol, developed technologies and barriers, and driving forces to commercialize the technologies are presented.

This chapter introduces the fundamental concepts of Genetic Algorithm (GA) and its applications, which have become an essential tool for solving optimization problems in a wide variety of RAMS fields. The main aim of this chapter is to set out the general and practical guidelines for designing GA, and GA applications are defined too. The first part of this chapter explains the principle of GA. The GA construction, genetic operators, evolution model and replacement strategy, stop condition, and advantages and disadvantages of the GAs are presented. The second part of the chapter focuses on GA applications. Reliability allocation for different types of systems is investigated, and simple, complex, and multilayer systems are studied. Modeling techniques are also explained. Reliability based-design optimization of structural components is then considered, optimization of maintenance and spare parts inventory are jointly investigated, and a production line problem is studied as an example. The system cost and availability are defined as target functions to analyze the system mentioned. Moreover, the principle of hybrid algorithms such as HGA and GA-PSO is introduced to improve the traditional GA capability and speed to find the optimal situation of the system. At the end of the chapter, the GA application to model and solve Industry 4.0 problems is investigated, and future works in this field are proposed.

Industrialization and a significant increase in energy requirement in the last decades have resulted in an increase in CO2 emission and consequently the global warming. Hence the environmental protection criteria enforced the scientific researches into the more efficient CO2 capture processes. In this case, increasing the efficiency of gas-liquid contact, e.g., increasing the contact time, contact area, and removing the mass transfer resistance, within the conventional and novel contactors plays the important role. Different novel contactors, e.g., rotating packed bed, mini- and micro-channels are three prototypes of the efficient contactors in gas-liquid processes. Besides the advantages of these contactors, industrialization of such processes is the main obstacle for real future applications. In this chapter, the main advantages, different types, basic principles and theoretical aspects, as well as fluid dynamics and design criteria of the aforementioned contactors were described.

The environment issues related to climate change and global warming have conducted researches toward the application of ionic liquids as scrubbing agent in CO2 capture and storage. The inherent tunable structure, excellent CO2 affinity, low volatility, high thermal and chemical stability, and large electrochemical window are the main advantages of ionic liquids compared to the traditional solvent in CO2 capture. This chapter is aimed to provide an insight on the potential and challenges on the application of ionic liquids in CO2 separation processes. In particular, the CO2 absorption efficiency including the equilibrium solubility, reaction rate, and selectivity is reviewed in more details. Specifically, chemisorption and physisorption of CO2 by ionic liquids have been investigated and the effects of cations, anions, and functional groups are reviewed. Finally, the deep eutectic solvents as a new class ionic liquid are briefly reviewed and challenges concerning ionic liquids have been listed and discussed.

Since CO2 is the most important greenhouse gas that progresses world warming and climate change, there is a global motivation to capture and reutilizing the released CO2 from industrial sectors. The solvent-based carbon capture is a proven technology from economic and operating perspectives to separate CO2 from gaseous streams. Although, the conventional solvents have acceptable thermodynamic and kinetic features, these materials are corrosive, tend to degrade thermally and chemically and cannot fulfill the desired performance in the industrial processes. In this regard, the modification of conventional solvents with nanosized particles to improve the effectivity is a practical solution to overcome the disadvantages of conventional amines. The solvent-based nanoparticle suspensions and nanoparticle organic hybrid material are the two main groups of nanomaterial-modified solvents that are used in CO2 capture. In this chapter, the concept and current progress in nanoparticle-modified solvents including synthesis method, stability mechanisms, mechanism of CO2 capture, and developed processes are discussed and the recent challenges and perspectives are also reviewed.