Engineering topics

Valentino, Lauren

Publications and source records attributed to Valentino, Lauren.

6 records

DOE OSTI2026

Physicochemical and Performance Characterization of Six Commercial Organic Solvent Nanofiltration Membranes

This work introduces a novel, gradient-free metamaterial design method based on Gaussian process regression to represent the density field of a unit cell. The dimension of the design space is determined by the covariance matrix dimension in the Gaussian process regression. We propose compressing this matrix using an autoencoder, enabling the decoder to generate the density field and effectively reduce the originally large design space to a lower-dimensional subspace. In this compressed space, we employ an active learning method, Bayesian Adaptive Direct Search (BADS), for efficient exploration of the design space. We demonstrate that for simple 2D designs aimed at maximizing unit cell stiffness, our method yields results comparable to those of standard topology optimization. Furthermore, we extend our approach to various mechanical problems, from linear elasticity to hyperelastic large deformation and elasto-plasticity under finite deformation, to 3D metamaterial design. This illustrates the method’s versatility and effectiveness across a range of applications.

Wu, Haoran↗

DOE OSTI2024

Adsorption Equilibrium, Kinetics, and Column Breakthrough Data for Aqueous Solutions of Binary-Acid and Ternary-Acid Mixtures of Acetic Acid, Butyric Acid, and Lactic Acid on IRN-78 Ion-Exchange Resin at Initial pH Levels of ∼3–7 and at 25–55 °C

This work is part of an effort to develop thermophysical property data and models supporting adsorptive process development for organic acid separation from a dilute aqueous solution of fermentation broth. It presents systematic experimental measurements for aqueous-phase adsorption equilibrium, kinetics, and column breakthrough for three binary-acid aqueous mixtures (acetic acid + lactic acid, butyric acid + lactic acid, and acetic acid + butyric acid) and one ternary-acid aqueous mixture (acetic acid + butyric acid + lactic acid) on Amberlite IRN-78 ion-exchange resin. The equilibrium measurements covered broad ranges of the initial acid concentration (100–400 mmol/L), initial pH (∼3–7), and temperature (25–55 °C). The equilibrium data for the binary-acid and ternary-acid aqueous mixtures indicate selective adsorption of lactic acid at initial pH levels of ∼3 and 4, while acetic acid and butyric acid are selectively adsorbed at initial pH levels of 5, 6, and 7. The subsequent kinetics and column breakthrough experiments were performed at 200 mmol/L with equimolar ratios, an initial pH of 6, and 25 °C. In conclusion, the measurements provide essential data sets for rigorous thermodynamic modeling and process simulation of adsorptive separation processes for organic acid separation from fermentation broth.

Adsorption↗

DOE OSTI2024

Adsorption Equilibrium, Kinetics, and Column Breakthrough Data of Acetic Acid, Butyric Acid, and Lactic Acid on IRN-78 Ion-exchange Resin at Initial pH ~3 – 7 and Temperature 25 – 55 °C

This work presents systematic aqueous-phase adsorption equilibrium, kinetics, and column breakthrough measurements with three key biointermediates that are common compounds in many bioprocesses. Adsorption equilibrium experiments were carried out with acetic acid, butyric acid, and lactic acid on a commercial ion-exchange resin, Amberlite IRN-78, at wide ranges of acid concentration (8-500 mmol/L), initial pH (similar to 3-7), and temperature (25-55 degrees C), simulating the effluent characteristics from different fermenter operations. The kinetics and column breakthrough experiments were conducted at an initial pH of 6 and a concentration of 200 mmol/L. The equilibrium study shows a higher loading at the initial pH < pK(a) and a lower loading at the initial pH > pK(a). Overall removal varies between 16 and 99% depending on the initial pH, temperature, and organic acid concentration and type. The study further indicates monolayer adsorption at the equilibrium pH > 10 and multilayer adsorption at the equilibrium pH < 6. The thermodynamic modeling of adsorption isotherm data was carried out using Langmuir and Freundlich isotherms. IRN-78 presents fast adsorption kinetics as the maximum loading was attained in <= 10 min and nearly the same breakthrough time for all three organic acids involved in this study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Sustainable Aviation Fuel from High-Strength Wastewater via Membrane-Assisted Volatile Fatty Acid Production: Experimental Evaluation, Techno-economic, and Life-Cycle Analyses

To reduce emissions from combustion of fossil fuels, sustainable aviation fuels (SAFs) have the potential to decarbonize the aviation sector. Redirecting wastes from conventional waste management practices and using them as cost-effective feedstocks for low-carbon fuels can reduce emissions from both waste disposal and fuel combustion. One approach is to upgrade wet wastes to SAF precursors, such as volatile fatty acids (VFAs). Here, in this study, novel membrane-assisted arrested methanogenesis was developed to convert high-strength wastewater to VFAs. Based on experimental results of VFA production, techno-economic and life-cycle analyses were conducted to estimate the potential economic and environmental benefits of SAF production from high-strength wastewater via VFAs. By evaluating three proposed scenarios for VFA production, a minimum production cost of VFA is achieved at $\$$0.60/kg VFA at a wastewater flow rate of 1100 MT/d. For the corresponding VFA-derived SAF, the estimated minimum fuel selling price is $\$$4.64/gasoline gallon equivalent. The life-cycle analysis shows that up to a 71% reduction in greenhouse gas emissions can be achieved relative to its fossil-counterpart along with lower water and fossil-fuel consumption.

09 BIOMASS FUELS↗

DOE OSTI2023

Capacitive Deionization for the Extraction and Recovery of Butyrate

Separation processes underpin chemical manufacturing, energy and fuel production, resource recovery, and water purification. Capacitive deionization (CDI) is an electrochemical separation technique based on electrosorption of charged species from an aqueous solution. This work reports the development and application of CDI for the extraction and recovery of butyrate, a carboxylate anion that is key to bioenergy production, among other applications. Electrosorption of sodium butyrate was evaluated using a bench-scale CDI system with activated carbon cloth electrodes. Applying an external potential (1.2 V) enhanced the adsorption capacity of activated carbon for butyrate by 250%. Reversible electrosorption was also demonstrated by extracting and recovering butyrate at 1.2 and 0 V, respectively. Performance metrics including butyrate adsorption and desorption capacities, adsorption and desorption rates, charge efficiency, energy consumption, and recovery are reported. Furthermore, this study also investigates the long-term operational stability of the system by cycling the cell more than 1000 times using 1.2 and 0 V for charging and discharging, respectively. Subsequent physicochemical characterization indicates increased oxygen content (from similar to 2% to >8%), decreased specific surface area (by similar to 20%), and decreased pore volume (by similar to 20%), attributed to carbon oxidation. Overall, this work informs the design of CDI for organic anion separation and recovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

2022 AI Testbed Expeditions Report

By exploiting the coherent properties of a light source, coherent diffraction imaging (CDI) is able to obtain the sample image at a nanoscale resolution using the measured diffraction pattern. Bragg Coherent Diffraction Imaging (BCDI) has become valuable for recovering the displacement and strain field of crystals, providing a valuable tool in material science and solid-state physics. X-ray ptychography is another emerging CDI technique that can produce a high-resolution image of the extended sample and has become popular in many research areas (e.g., materials science, biology, electronics, and optics characterization). CDI including BCDI and ptychography has become an established technique in Synchrotron Facilities including the Advanced Photon Source (APS) and will greatly benefit from the 100x coherent flux increase of the upcoming APS Upgrade (APSU). The current image formation process in CDI employs iterative phase retrieval algorithms, which is a time-consuming and computationally expensive process. Especially after APSU, the traditional iterative methods will not be able to match the experimental data acquisition speed. We employ deep learning (DL) approach to replace the iterative approaches, therefore allowing hundreds of times faster recovery of the object. We developed AutoPhaseNN, a DL-based approach which learns to solve the inverse problem without labeled data. Taking 3D BCDI as a representative technique, AutoPhaseNN has been demonstrated to be one hundred times faster than traditional iterative phase retrieval methods while providing comparable image quality. The current network is trained with 64 x 64 x 64 data size, to achieve higher resolution imaging, we will need to scale the network to input and train/infer 3D arrays of size 256 x 256 x 256 (today) and of size 2560x2560x2560 (APSU). However, the scalability of the network is restricted due to the memory-intensive training process. To perform the training for a 256 x 256 x 256 data size, the required memory exceeds the capacity of the current machine. In this project, we explore using Sambanova system to train the network for the direct data inversion for CDI.

36 MATERIALS SCIENCE↗