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Organic electrochemical transistors (OECTs) have enormous potential for use in biosignal amplifiers, analyte sensors, and neuromorphic electronics owing to their exceptionally large transconductance. However, it is challenging to simultaneously achieve high charge carrier mobility and volumetric capacitance, the two most important figures of merit in OECTs. Herein, a method of achieving high-performance OECT with donor-acceptor conjugated copolymers by introducing fluorine units is proposed. A series of cyclopentadithiophene-benzothiadiazole (CDT-BT) copolymers for use in high-performance OECTs with enhanced charge carrier mobility (from 0.65 to 1.73 cm

Classification of technological uncertainties using stakeholder-defined decision criteria to assess and identify key sustainability hurdles in emerging carbon dioxide utilization (CDU) technologies.

By simultaneously considering the powerful electron-accepting capability of pyridine and the strong selenophene (Se)–Se interaction of selenophene, we present here the design and synthesis of both pyridine and selenophene-containing, pyridine-flanked diketopyrrolopyrrole and 4,7-di(selenophen-2-yl)-2,1,3-benzothiadiazole-based copolymers (P1 and P2) for use in organic field-effect transistors, where P1 has a shorter branched position of the side chains in relation to the backbone as compared to P2. The structure–property relationships associated with branching point engineering in the copolymers are established by applying a range of technical analyses. Overall, P1 interestingly exhibits both a higher hole and electron mobility of up to 1.2 cm2 V−1 s−1 and 0.21 cm2 V−1 s−1, respectively, which are over one order of magnitude higher than that of P2. This observation demonstrates the opposite view of the branching points away from the backbone for high-performing organic field-effect transistors. Besides, by doping sodium bicarbonate and molybdenum trioxide with electrodes, we succeeded in affecting the transition from ambipolarity of P1 to unipolar n- and p-type characteristics, respectively. These findings improve our understanding of both the role of branching point engineering in enhancing the carrier transport and the use of doping for switching the polarity of P1.

Heterotrophic cultivation can be a preferred option for commercial microalgal biorefinery owing to its significantly higher productivity and lipid content leading to high value products. However, l...

In this study, the effects of ship motion on the CO2 absorption performance of the offshore columns (i.e., the separation columns installed on offshore floating units) are investigated. Specifically, a pilot scale column with 0.4 m diameter and 4 m packed height is used, and NaOH/air system is implemented to measure the CO2 absorption performance of the column under both vertical and offshore conditions. The experimental results obtained from the vertical conditions are used to validate the mathematical model which can compute the effective surface area of the separation columns. Then, such model is used to produce the reference values for the experimental results obtained from the various offshore conditions, where tilt angle, roll motion conditions, liquid load, gas factor, liquid surface tension and liquid viscosity are used as the design variables. The reduction factor is proposed, which can describe the change in the absorption performance of the offshore columns with respect to the important factors. From our study, the tilt angle was shown to be the most important factor, and the mass transfer efficiency was reduced by 8.4% in average (compared to the value obtained under the vertical condition) when the tilt angle was 6°. It was also shown that the roll motion can enhance the absorption performance of offshore columns under certain operating conditions, and the maximum improvement (6.7%) in the mass transfer efficiency was achieved when the liquid load, roll motion amplitude and period were 14 m3/m2 hr, 8° and 45 s, respectively.

In this paper, a parallel neural network architecture is proposed to improve the performance of neural-network-based nonlinear principal component analysis. There exist two typical approaches for such analysis: simultaneous extraction of principal components using a single autoassociative neural network (also known as autoencoder), and sequential extraction using multiple neural networks in series. The proposed architecture can be obtained by systematically pruning the network connections of a fully connected autoassociative neural network, resulting in decoupled neural networks. As a result, more independent (i.e., less correlated) principal components can be obtained than the simultaneous extraction approach. The proposed architecture can be also viewed as a rearrangement of multiple neural networks for the sequential extraction in a parallel setting, and thus, the network training becomes more efficient. Simulation case studies are performed to illustrate the advantages of the proposed architecture, and it was shown that it is particularly beneficial for deep neural networks.

Microalgal biomass is considered to be a promising renewable energy source to replace fossil fuel given its simple growth mechanism and high productivity per unit area. Nevertheless, microalgal bio...

In this article, a conceptual process design for the production of lactide is proposed, where an one-step gas phase synthesis route is adopted. Then, the proposed process is evaluated and compared ...

Fuel cell auxiliary power unit (FC-APU) system for heavy-duty trucks has been attracting much attention as an alternative to engine idling to reduce pollutant emissions. In this system, a reformer, which converts diesel, the main fuel of heavy-duty trucks, into syngas suitable for the fuel cell, is essential. A kinetic model of the reforming can be helpful in tasks such as the reactor design and system operation, but it is highly challenging to develop such a model due to the complexity of the reforming reaction network. Thus, in this study, we propose a systematic approach to build a reduced kinetic model of the diesel autothermal reformer. Specifically, microreactor experiments are conducted using a commercial diesel fuel, where the temperature, oxygen-to-carbon ratio (OCR), and steam-to-carbon ratio (SCR) are varied. Then, based on the composition measurements of the exit gas, optimal kinetic models are constructed by iteratively solving a parameter estimation problem for the candidate kinetic models with varying number of reactions. To reduce the number of candidates significantly, lumping techniques are used to generate reduced kinetic models. A stochastic optimization method is employed for the parameter estimation in which the concept of cross-entropy is used. The optimal reduced kinetic models with 3 and 4 reactions showed a consistency with the literature in terms of the reactions included. The accuracy of optimal reduced kinetic models increased, showing 21% improvement as the number of reactions increased from 3 to 4.

Advanced flash stripper (AFS) has been suggested as a process alternative for conventional absorption-based post-combustion carbon capture process, which can significantly reduce the solvent regeneration energy. However, such reduction is enabled through energy integration achieved by two additional heat exchangers, which can pose serious challenges to the control and operation of carbon capture processes. For complex energy-integrated processes, simple decentralized control scheme, where multiple proportional–integral–derivative (PID) controllers are employed, typically shows limited control performances. Thus, this study aims to propose an effective control structure for the carbon capture process with AFS, which can regulate the process under various dynamic scenarios involving significant changes in operational variables such as flue gas flowrate and carbon capture rate. Specifically, a dynamic model for the post-combustion carbon capture process is built in gPROMS, where 30 wt% Monoethanolamine (MEA) solvent is used in the absorber. Then, step responses of the controlled outputs with respect to the manipulated and disturbance inputs are analyzed to characterize the dynamic behavior of such process. A model predictive control (MPC) strategy is proposed on the basis of the understanding from the analysis. Finally, the closed-loop performances of the proposed control strategy and decentralized PID controllers are compared to demonstrate the effectiveness of the MPC strategy. The MPC strategy demonstrates that it can track the set-point change at least 20 min faster than the PID strategies, and stabilize the stripper section about 200 min faster.

In this article, the statistical process monitoring problem of the Tennessee Eastman process is considered using deep learning techniques. This work is motivated by three limitations of the existing works for such problem. First, although deep learning has been used for process monitoring extensively, in the majority of the existing works, the neural networks were trained in a supervised manner assuming that the normal/fault labels were available. However, this is not always the case in real applications. Thus, in this work, autoassociative neural networks are used, which are trained in an unsupervised fashion. Another limitation is that the typical dataset used for the monitoring of the Tennessee Eastman process is comprised of just a small number of data samples, which can be highly limiting for deep learning. The dataset used in this work is 500-times larger than the typically-used dataset and is large enough for deep learning. Lastly, an alternative neural network architecture, which is called parallel autoassociative neural networks, is proposed to decouple the training of different principal components. The proposed architecture is expected to address the co-adaptation issue of the fully-connected autoassociative neural networks. An extensive case study is designed and performed to evaluate the effects of the following neural network settings: neural network size, type of regularization, training objective function, and training epoch. The results are compared with those obtained using linear principal component analysis, and the advantages and limitations of the parallel autoassociative neural networks are illustrated.

The formulation of a kinetic model for a complex reaction network typically yields reaction rates which vary over orders of magnitude. This results in time scale separation that makes the model inherently stiff. In this work, a graph-theoretic framework is developed for time scale decomposition of complex reaction networks to separate the slow and fast time scales, and to identify pseudo-species that evolve only in the slow time scale. The reaction network is represented using a directed bi-partite graph and cycles that correspond to closed walks are used to identify interactions between species participating in fast/equilibrated reactions. Subsequently, an algorithm which connects the cycles to form the pseudo-species is utilized to eliminate the fast rate terms. These pseudo-species are used to formulate reduced, non-stiff kinetic models of the reaction system. Two reaction systems are considered to show the efficacy of this framework in the context of thermochemical and biochemical processing.

A key feature of amine-based post-combustion CO2 capture process is a wide operating range induced by periodic load changes in power plants, which necessitates flexible operation. One possible approach to enhance the operational flexibility is to design a reliable controller that can effectively regulate the process over the operating range. To this end, in this study, a robust model predictive controller is designed by analyzing the dynamic characteristics of a post-combustion CO2 capture process. Specifically, gap metric analysis is performed to analyze the sensitivity of the process. From this analysis, optimal operating conditions are identified by evaluating similarity among the dynamics around different operating conditions. Then, a single linear model predictive controller is designed on the basis of the linear approximation of the original nonlinear model at the chosen conditions. Finally, the effectiveness of the controller is illustrated through a case study on an example CO2 capture process.

This research suggests a three-stage model framework for the design of a microalgae-based biofuel supply chain to meet the goal of economic commercialization. First, the design stage decides the spatial layouts and dimensions of each scale of biorefineries and economic analyses are done to estimate the capital costs and operating costs for different design options. Using the spatial dimensions determined in the first stage, the second stage selects the candidate locations for the biorefineries using an geographic information system (GIS) based site evaluation. This stage screens the available land area for the biorefineries, and also reduces the computational burden of the latter stage. In the mathematical optimization stage, a mixed-integer linear programming optimization model is formulated to make multi-period strategic and tactical decisions of the supply chain under the total cost minimization objective. Since the formulated problem is computationally intensive, a two-stage decomposition solution strategy is proposed to solve the problem in a reasonable time. The model framework is demonstrated through a case study cast in Texas, U.S., with a time horizon of ten years, using the suggested decomposition method. As a result, the minimum fuel selling price (MFSP) of microalgae-based biodiesel is calculated as $10.92/galbiodiesel, which is about three times higher than the current biodiesel price $3.51/galbiodiesel. To reduce the cost, the strategies of biomass storage and maximum delivery are investigated to deal with the productivity fluctuation. In the scenario analysis, the underutilization of production capacities is alleviated resulting in reduced MFSP of $7.90/galbiodiesel, $7.92/galbiodiesel, and $7.43/galbiodiesel respectively. Clearly, for economic feasibility, the production cost should be further reduced by developing more cost-efficient technologies and integrating high-value coproducts into the biorefinery portfolio.

Microalgae is a promising biomass source for renewable fuels and chemicals production. To describe microalgal behavior and improve their cultivation, various kinetic models have been proposed. However, previous works have focused on biomass formation and lipids production only, even though carbohydrates and proteins are also important products, not only for understanding the metabolic behavior of microalgae but also for enhancing the economic viability through value-added side products. In this study, a new mathematical model is proposed to explain core biological mechanisms of growth and macromolecules syntheses based on the central metabolism of carbon and nitrogen. In the model, microalgal growth is separated as hyperplasia and hypertrophy, to describe the cell growth more precisely under nutrient-replete and -deplete conditions. Sensitivity analysis performed using the model indicates that cell state (e.g., cell death rate) has a strong effect on the lipid production explaining the difficulty of reproducing a microalgae culture experiment.

In this paper, a method to systematically synthesize control configurations with favorable structural coupling is developed, using relative degree as a measure of such coupling. Initially, an integer optimization problem is formulated to identify optimal distributions of inputs and outputs (decentralized control configurations) that minimize the overall structural coupling in the network. Then, a hierarchical clustering procedure, which allows identifying groups of inputs and outputs that are strongly connected topologically (block decentralized control configurations), is proposed. The application of the method is illustrated through an example process network.

This paper focuses on the analysis of complex material integrated plants, which may consist of multiple interconnected recycle structures, with material flows spanning multiple orders of magnitude. For such plants, there is a potential of exhibiting multiple time scale material dynamics. An analysis framework is developed to identify the time scales where each process variable evolves and the potential manipulated inputs available in each time scale. A novel graph formalism is proposed to capture the material flow structure of such plants. Then, a graph-theoretic algorithm is developed, which mimics a singular perturbation-based time scale decomposition. The application of the proposed framework is illustrated using two example chemical plants.

In this paper, a scalable framework is developed to analyze complex process networks consisting of multiple material integration loops. Using information on the orders of magnitude of different material flows, the framework identifies the time scales where different process units evolve, and the manipulated inputs available in each time scale, in an automated fashion. The application of the framework is illustrated via a case study on an example process network.

In this study, nonlinear control of high duty counter-current heat exchanger is considered. The dynamics of this system can be captured by first order hyperbolic partial differential equations (PDEs), which are stiff due to the high rate of heat transfer. The potential of multi-time scale dynamics is discussed, and model reduction is performed using singular perturbations to obtain non-stiff PDE models which are valid in each time scale. Three controllers are designed and compared in the simulation case study: input/output linearizing controllers on the basis of original model and reduced order model, and a proportional-integral controller. The nonlinear controller designed using the reduced order model showed a superior performance compared to the others, especially in the presence of large modeling errors and disturbances.

In this paper, a divisive hierarchical clustering method is proposed to address the problem of pairing of manipulated inputs and controlled outputs. Specifically, an integer nonlinear optimization problem is formulated to identify groups of inputs and outputs with strong structural coupling quantified by relative degrees. This optimization problem can be solved in a hierarchical manner to generate a hierarchy of block decentralized control configurations. The application of the method is illustrated through a case study on an example chemical process network.

In this paper, we propose a graph-theoretic algorithm that can be used to analyze complex chemical processes comprising of multiple energy integration loops. Such networks are known to exhibit dynamics in multiple time scales. The algorithm uses information on the order of magnitude of the different energy flows and determines automatically the time scales where the units evolve, the manipulated inputs acting in the different time scales and the form of the reduced order models in each time scale. The application of the algorithm is illustrated through a case study of a benchmark chemical process.