Smart manufacturing inspired approach to research, development, and scale-up of electrified chemical manufacturing systems.

As renewable electricity becomes cost competitive with fossil fuel energy sources and environmental concerns increase, the transition to electrified chemical and fuel synthesis pathways becomes increasingly desirable. However, electrochemical systems have traditionally taken many decades to reach commercial scales. Difficulty in scaling up electrochemical synthesis processes comes primarily from difficulty in decoupling and controlling simultaneously the effects of intrinsic kinetics and charge, heat, and mass transport within electrochemical reactors. Tackling this issue efficiently requires a shift in research from an approach based on small datasets, to one where digitalization enables rapid collection and interpretation of large, well-parameterized datasets, using artificial intelligence (AI) and multi-scale modeling. In this perspective, we present an emerging research approach that is inspired by smart manufacturing (SM), to accelerate research, development, and scale-up of electrified chemical manufacturing processes. The value of this approach is demonstrated by its application toward the development of CO2 electrolyzers.

Rapid field assessment of RO desalination of brackish agricultural drainage water.

Rapid field evaluation of RO feed filtration requirements, selection of effective antiscalant type and dose, and estimation of suitable scale-free RO recovery level were demonstrated using a novel approach based on direct observation of mineral scaling and flux decline measurements, utilizing an automated Membrane Monitor (MeMo). The MeMo, operated in a stand-alone single-pass desalting mode, enabled rapid assessment of the adequacy of feed filtration by enabling direct observation of particulate deposition on the membrane surface. The diagnostic field study with RO feed water of high mineral scaling propensity revealed (via direct MeMo observation) that suspended particulates (even for feed water of turbidity <1 NTU) could serve as seeds for promoting surface crystal nucleation. With feed filtration optimized, a suitable maximum RO water recovery, with complete mineral scale suppression facilitated by an effective antiscalant dose, can be systematically and directly identified (via MeMo) in the field for a given feed water quality. Scale-free operating conditions, determined via standalone MeMo rapid diagnostic tests, were shown to be applicable to spiral-would RO system as validated via both flux decline measurements and ex-situ RO plant membrane scale monitoring. It was shown that the present approach is suitable for rapid field assessment of RO operability and it is particularly advantageous when evaluating water sources of composition that may vary both temporally and across the regions of interest.

Investigation of film surface roughness and porosity dependence on lattice size in a porous thin film deposition process.

The dependence of film surface roughness and porosity on lattice size in a porous thin film deposition process is studied via kinetic Monte Carlo simulations on a triangular lattice. For sufficiently large lattice size the steady-state value of the expected film porosity has a weak dependence on the lattice size and the steady-state value of the expected surface roughness square varies linearly with lattice size. An analysis of the film morphology based on a stochastic partial differential equation description of the film surface morphology supports and explains the findings of the numerical simulations.

Predictive control of crystal size distribution in protein crystallization.

This work focuses on the modelling, simulation and control of a batch protein crystallization process that is used to produce the crystals of tetragonal hen egg-white (HEW) lysozyme. First, a model is presented that describes the formation of protein crystals via nucleation and growth. Existing experimental data are used to develop empirical models of the nucleation and growth mechanisms of the tetragonal HEW lysozyme crystal. The developed growth and nucleation rate expressions are used within a population balance model to simulate the batch crystallization process. Then, model reduction techniques are used to derive a reduced-order moments model for the purpose of controller design. Online measurements of the solute concentration and reactor temperature are assumed to be available, and a Luenberger-type observer is used to estimate the moments of the crystal size distribution based on the available measurements. A predictive controller, which uses the available state estimates, is designed to achieve the objective of maximizing the volume-averaged crystal size while respecting constraints on the manipulated input variables (which reflect physical limitations of control actuators) and on the process state variables (which reflect performance considerations). Simulation results demonstrate that the proposed predictive controller is able to increase the volume-averaged crystal size by 30% and 8.5% compared to constant temperature control (CTC) and constant supersaturation control (CSC) strategies, respectively, while reducing the number of fine crystals produced. Furthermore, a comparison of the crystal size distributions (CSDs) indicates that the product achieved by the proposed predictive control strategy has larger total volume and lower polydispersity compared to the CTC and CSC strategies. Finally, the robustness of the proposed method (achieved due to the presence of feedback) with respect to plant-model mismatch is demonstrated. The proposed method is demonstrated to successfully achieve the task of maximizing the volume-averaged crystal size in the presence of plant-model mismatch, and is found to be robust in comparison to open-loop optimal control strategies.

Analysis of nitric oxide consumption by erythrocytes in blood vessels using a distributed multicellular model.

Multiple sets of experimental data have shown that the red blood cell (RBC) consumes nitric oxide (NO) about 600-1000-fold slower than the equivalent concentration of cell-free hemoglobin (Hb). Diffusion barriers of various sources have been suggested to explain this observation. In this work, a multicellular, spatially distributed, two-dimensional model, that describes the production, transport, and consumption of NO in blood vessels and the surrounding tissue, is developed. The model is used to assess the relative significance of NO transport barriers that reduce the rate of NO consumption in the blood. Unlike previous models of this system, the model developed here accounts explicitly for the presence of, and interactions among, a population of RBCs inside the lumen of the blood vessel and is, therefore, better suited to analyze, quantitatively, the contribution of each transport barrier as NO diffuses from its site of synthesis to the interior of the RBCs where it interacts with Hb. The model, which uses experimentally derived parameters, shows that extracellular unstirred boundary layer diffusion alone cannot account for the reduced NO consumption by RBC compared to an equivalent concentration of cell-free Hb. Since this result is reached using a two-dimensional representation of the RBCs, which overestimates the importance of the boundary layer diffusion resistance, it would be expected that in the real three-dimensional case, diffusion through the extracellular boundary layer would contribute even less to the overall mass transfer resistance. Consistent with recent experimental findings, the results of our model suggest that, under physiological conditions, transmembrane (membrane and its associated cytoskeleton layer) diffusion limitations in RBCs represent a key source of resistance for NO uptake by RBCs.