Exploring nontraditional LSTM architectures for modeling demethanizer column operations

Digital twins have recently attracted attention as a new technology that can facilitate the digital transformation of process industries. It may provide live, or near real-time, information and insights into the process and may be used for monitoring, control and optimization purposes. In this study, a digital twin has been developed for modelling the demethanizer column of a NGL separation plant. Based on a non-conventional Long Short-Term Memory (LSTM) neural network arrangement, the surrogate model has been trained and validated using data obtained by the process simulator Aspen HYSYS. Model prediction can be obtained using only readily available variables as input data, ensuring easy and cost-effective implementation. Measurement noises have been considered in order to mimic real-world measurements in a real plant. In both steady-state and transient conditions, the developed demethanizer digital twin accurately reconstructs the separation operation, including compositions, temperatures, and pressures in the reboiler and all column stages

Brewer's spent grain, coffee grounds, burdock, and willow-four examples of biowaste and biomass valorization through advanced green extraction technologies

This paper explores the transformation of biowastes from food industry and agriculture into high-value products through four examples. The objective is to provide insight into the principles of green transition and a circular economy. The first two case studies focus on the waste generated from the production of widely consumed food items, such as beer and coffee, while the other two examine the potential of underutilized plants, such as burdock and willow, as sources of valuable compounds. Phenolic compounds are the main target in the case of brewer's spent grain, with p-coumaric acid and ferulic acid being the most common. Lipids are a possible target in the case of spent coffee grounds with palmitic (C16:0) and linoleic (C18:2) acid being the major fatty acids among those recovered. In the case of burdock, different targets are reported based on which part of the plant is used. Extracts rich in linoleic and oleic acids are expected from the seeds, while the roots extracts are rich in sugars, phenolic acids such as chlorogenic, caffeic, o-coumaric, syringic, cinnamic, gentisitic, etc. acids, and, interestingly, the high-value compound epicatechin gallate. Willow is well known for being rich in salicin, but picein, (+)-catechin, triandrin, glucose, and fructose are also obtained from the extracts. The study thoroughly analyzes different extraction methods, with a particular emphasis on cutting-edge green technologies. The goal is to promote the sustainable utilization of biowaste and support the green transition to a more environmentally conscious economy.info:eu-repo/semantics/publishedVersio

Predictive control of an activated sludge process for long term operation

The application of a multivariable predictive controller to an activated sludge process is discussed in this work. Emphasis is given to the model identification and the long term assessment of the controller efficiency in terms of economical and environmental performances. A recurrent neural network model is developed for the identification problem and the dynamic matrix control is chosen as suitable predictive control algorithm for controlling the nitrogen compounds in the bioreactor. Using the Benchmark Simulation Model No. 1 as virtual platform, different predictive controller configurations are tested and further improvements are achieved by controlling the suspended solids at the end of the bioreactor. Based on the simulation results, this work shows the potentiality of the dynamic matrix control that together with a careful identification of the process, is able to decrease the energy consumption costs and, at the same time, reduce the ammonia peaks and nitrate concentration in the effluent

Investigation of Seasonal Variation in Fatty Acid and Mineral Concentrations of Pecorino Romano PDO Cheese: Imputation of Missing Values for Enhanced Classification and Metabolic Profile Reconstruction

Seasonal variation in fatty acids and minerals concentrations was investigated through the analysis of Pecorino Romano cheese samples collected in January, April, and June. A fraction of samples contained missing values in their fatty acid profiles. Probabilistic principal component analysis, coupled with Linear Discriminant Analysis, was employed to classify cheese samples on a production season basis while accounting for missing data and quantifying the missing fatty acid concentrations for the samples in which they were absent. The levels of rumenic acid, vaccenic acid, and omega-3 compounds were positively correlated with the spring season, while the length of the saturated fatty acids increased throughout the production seasons. Concerning the classification performances, the optimal number of principal components (i.e., 5) achieved an accuracy in cross-validation equal to 98%. Then, when the model was tasked with imputing the lacking fatty acid concentration values, the optimal number of principal components resulted in an R2 value in cross-validation of 99.53%

Brewer´s Spent Grain to Bioethanol Through a Hybrid Saccharification and Fermentation Process

Brewer´s spent grain, without being pre-treated, has been investigated for bioethanol production through a Hybrid Saccharification and Fermentation (HSF) process with high solid loading. HSF experiments were performed in a 2 L bioreactor where Cellic ® CTec2 was used to perform the enzymatic hydrolysis, and Saccharomyces Cerevisiae was used for the fermentation. The reaction environment was first set to favour saccharification. Then, after 26 h, the reactor was inoculated with the yeast. The results evidenced the presence of glucose, xylose, and arabinose after the conversion of cellulose and hemicellulose and a rapid depletion of glucose after adding the yeast. The pentoses were also consumed, but with a much slower reaction rate. Almost four hours after adding the yeast, the amount of ethanol had reached a maximum and then began to decrease as microorganisms began to use ethanol as a substrate after glucose depletion. The obtained ethanol yield, evaluated with respect to the theoretical value, was equal to 72%

Development of Hybrid Models for a Vapor-Phase Fungi Bioreactor

This study is aimed at the development of a model for an experimental vapour-phase fungi bioreactor, which could be derived in a simple way using the available measurements of a pilot-plant reactor, without the development of ad hoc experiments for the evaluation of fungi kinetics and the estimation of parameters related to biofilm characteristics. The proposed approach is based on hybrid models, obtained by the connection of the mass balance equation (used in traditional phenomenological models) with a feedforward neural network (used in black-box modelling), and the proper use of statistical tools for the model assessment and system understanding. Two different hybrid models were developed and compared by proper performance indexes, and their capability to predict the biological complex phenomena was demonstrated and compared to that of a first-principle model

A Gain-Scheduling PI Control Based on Neural Networks

This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC) formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR), considering both single-input single-output (SISO) and multi-input multi-output (MIMO) control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection

Conversion of glucose and sorbitol in the presence of Ru/C and Pt/C catalysts

The conversion of glucose and sorbitol in the presence of Ru and Pt catalysts supported on carbon was carried out at different pressure and temperature conditions, using a batch and a semi-batch reactor. Attempts were made to improve the selectivity of glycols and alcohols (ethanol), introducing a promoter and inhibiter of the hydrogenolysis in the reactant mixture. On the basis of these results, which confirm the higher activity of Ru with respect to Pt, and the important role of an inhibitor like sulphur, the mechanism driving these reactions and the promising thermocatalytic conditions are clearer