Application of AI in Chemical Engineering

Hajjar, Zeinab; Tayyebi, Shokoufe; EghbalAhmadi, Mohammad Hosein

doi:10.5772/intechopen.76027

Cited by 12 publications

(11 citation statements)

References 66 publications

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“…The topology used was a multilayer feed‐forward net, which is commonly used to model chemical engineering systems, [ 27,39 ] with just one hidden layer (Figure 1). The activation function used was the ‘tansig’ function, which is the package's default.…”

Section: Methodsmentioning

confidence: 99%

“…Artificial neural networks are presented as an alternative for the classical models to solve complex problems and are widely used to model chemical engineering processes. [ 26,27 ] This modelling has been used in different mass transfer processes such as adsorption. [ 24,28–32 ] Regardless of the highly nonlinear behaviour and the presence of several input variables, the ANNs had the capability to successfully describe the adsorption process.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the drying kinetics of brewer's spent grain (BSG) using artificial neural networks and traditional empirical models

2022

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Brewer's spent grain (BSG) is the main by‐product of the brewing industry. BSG can have diverse end‐users and has a high moisture level. To guarantee good conditions for storage and trade, it is necessary to remove the moisture from the material and use the proper method for the drying process. The phenomenon of water content removal is represented by mathematical models. Empirical and phenomenological models, as well as artificial neural networks (ANN) can be employed for this purpose. Here, we compare the fitted curves between empirical models (such as Page, Midilli–Kucuk, and Newton models) and an artificial neural network. The fits were investigated quantitatively by the analysis of the mean squared error (MSE) and determination coefficient (R2). The residues generated were analyzed qualitatively by the plots of histograms and qqplots (quantil vs. quantil). From the results that were obtained, it is possible to conclude that the ANN model had the best performance when compared to the empirical and semi‐empirical models studied, with the lowest values of MSE and the highest values of the R2 (0.999). The net presented adequate estimations for intermediate data as well, proving its use as a proper empirical model for the prediction of moisture data at several temperatures.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of the drying kinetics of brewer's spent grain (BSG) using artificial neural networks and traditional empirical models

2022

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“…The chemical industry is increasingly interested in using AI to address challenges in process modeling, optimization, control, and fault detection and diagnosis (Hajjar et al., 2016). Many of those aspects are related to the environmental, economic, and social sustainability of the chemical industry.…”

Section: Introductionmentioning

confidence: 99%

Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework

Ming-yang

Lan

Yao

2021

J of Industrial Ecology

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Artificial intelligence (AI) is an emerging technology that has great potential in reducing energy consumption, environmental burdens, and operational risks of chemical production. However, large‐scale applications of AI are still limited. One barrier is the lack of quantitative understandings of the potential benefits and risks of different AI applications. This study reviewed relevant AI literature and categorized those case studies by application types, impact categories, and application modes. Most studies assessed the energy, economic, and safety implications of AI applications, while few of them have evaluated the environmental impacts of AI, given the large data gaps and difficulties in choosing appropriate assessment methods. Based on the reviewed case studies in the chemical industry, we proposed a conceptual framework that encompasses approaches from industrial ecology, economics, and engineering to guide the selection of performance indicators and evaluation methods for a holistic assessment of AI's impacts. This framework could be a valuable tool to support the decision‐making related to AI in the fundamental research and practical production of chemicals. Although this study focuses on the chemical industry, the insights of the literature review and the proposed framework could be applied to AI applications in other industries and broad industrial ecology fields. In the end, this study highlights future research directions for addressing the data challenges in assessing AI's impacts and developing AI‐enhanced tools to support the sustainable development of the chemical industry.

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“…They have been widely used in the field of chemical engineering, for modeling, process control, and classification, including several branches such as artificial neural networks, fuzzy logic, genetic algorithms, expert systems, and hybrid systems. 8 This can be found, for example, in the simulation of a semi-industrial thickener plant based on computational fluid dynamics to investigate the effects of the thickener on the parameters’ performance. 3 In recent years, the mining industry in Chile has begun to use and observe an increase in interest in artificial intelligence techniques in processes such as the estimation of copper production, where traditional methods tend to lack certainty and knowledge.…”

Section: Introductionmentioning

confidence: 99%

Design of a sensor based on neural networks to determine sludge level of industrial thickeners

Leiva

Acuña

Leiva

2020

International Journal of Distributed Sensor Networks

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In copper mining, there are two main separation processes: leaching for oxidized minerals and flotation for sulfide minerals. In Chile, the increase of sulfide minerals in the deposits and the decrease of oxidized minerals have led to an increase in the investigation of flotation processes and the optimization of their associated operations. One of the concentration processes is the use of thickeners, whose main objective is to treat the tailings that leave the plants in pulp form with approximately 30% solids and obtain a pulp with a concentration greater than 50% with clear water flow. The recovery of water is the main goal, so knowing the concentration profiles of solids and sedimentation is crucial. However, the characteristics of the pulp and the operation of the thickeners are complex because a great variety of forms can be found in the concentration profile of said pulp. This limits conventional measurement techniques and makes it difficult to apply deterministic models to the solids profile, given the high nonlinearities and variability of the system. In this article, a solution is proposed by developing a sensor that allows the online estimation of sludge level and concentration of solids, based on a model of neural networks (with the model of Maxwell for dispersions), allowing to measure the solids profile regardless of the operating conditions. The structure selected has nine inputs with a hidden layer, two neurons, and two outputs being trained with tailings from Chuquicamata, obtaining the information from a 50 L pilot with 10-electrode bar of 60 cm of length, resulting in an estimation error of 0.8 cm with a network of 26 parameters.

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Application of AI in Chemical Engineering

Cited by 12 publications

References 66 publications

Investigation of the drying kinetics of brewer's spent grain (BSG) using artificial neural networks and traditional empirical models

Investigation of the drying kinetics of brewer's spent grain (BSG) using artificial neural networks and traditional empirical models

Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework

Design of a sensor based on neural networks to determine sludge level of industrial thickeners

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