Application of AI in Chemical Engineering

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields

Comparative Study on Adsorptive Desulfurization of Thiophenic Compounds over Terephthalic Acid-Based and Trimesic Acid-Based Metal–Organic Frameworks

Aromatic sulfur compounds present in liquid fuels in the form of thiophenes are converted into sulfur oxides (SOx) during the combustion of fuels and cause major environmental problems such as acid rain, global warming, and air pollution. The adsorptive desulfurization of four aromatic sulfur compoundsthiophene, benzothiophene, dibenzothiophene, and 4,6-dimethyl dibenzothiophenefrom n-octane was investigated in the current study over five typical metal–organic frameworks (MOFs), namely MIL-53(Cr, Al, Fe), Cu-BDC, and HKUST-1, which were successfully synthesized and characterized using XRD, FT-IR, SEM, and N2 adsorption–desorption. The results indicate that for ADS of thiophene and BT, the adsorption capacity of MOFs increases in the order MIL – 53(Fe) < Cu – BDC < HKUST – 1 < MIL – 53(Al) < MIL – 53(Cr), for DBT, it increases in the order Cu – BDC < MIL – 53(Fe) < HKUST – 1 < MIL – 53(Al) < MIL – 53(Cr), and for DMDBT, it increases in the order HKUST – 1 < Cu – BDC < MIL – 53(Fe) < MIL – 53(Al) < MIL – 53(Cr). It is evident that among the synthesized MOFs, MIL-53(Cr) demonstrated the highest adsorptive desulfurization capacity upon removing sulfur compounds from model oil due to its high surface area and suitable pore diameter, with sulfur removal performance of 29, 39, 61, and 88% for the adsorption of thiophene, benzothiophene, dibenzothiophene, and 4,6-dimethyldibenzothiophene, respectively. Adsorption data were checked through kinetic models and isotherms, and it has been shown that the adsorption kinetics follow the pseudo-second-order kinetic model, and the adsorption isotherm is better described by the Langmuir equation

Artificial intelligence techniques for modeling and optimization of the HDS process over a new graphene based catalyst

<p>A Co-Mo/graphene oxide (GO) catalyst has been synthesized for the first time for application in a defined hydrodesulfurization (HDS) process to produce sulfur free naphtha. An intelligent model based upon the neural network technique has then been developed to estimate the total sulfur output of this process. Process operating variables include temperature, pressure, LHSV and H₂/feed volume ratio. The three-layer, feed-forward neural network developed consists of five neurons in a hidden layer, trained with Levenberg–Marquardt, back-propagation gradient algorithm. The predicted amount of residual total sulfur is in very good agreement with the corresponding experimental values revealing a correlation coefficient of greater than 0.99. In addition, a genetic algorithm (GA) has been employed to optimize values of total sulfur as well as reaction conditions.</p

RES-Q an Ongoing Project on Municipal Solid Waste Management Program for the Protection of the Saniq River Basin in Southern Lebanon

Lebanon is currently facing an unprecedented mix of social, economic, and political crises, which exacerbate many public health and environmental health problems. Among these, solid waste management (SWM) is considered one of the biggest challenges that Lebanon has been facing for the past two decades. In the absence of national guidelines and ministerial action, SWM is a responsibility of local municipalities. In this paper, we describe the development of a technology-based Waste Management System (WMS) in an area of 43 villages in southern Lebanon. The project is inspired to the paradigms of circular economy and smart cities, and it aims to define affordable and efficient strategies to address SWM. The driving factor in defining the strategies is economic and environmental sustainability, as Lebanon imports most of its resources in hard currency, which is becoming less and less available. The lessons learned from this project can be transposed in other areas and countries with limited financial resources, representing an important paradigm for the WMSs in many areas of the world