Journal of advanced manufacturing and processing最新文献

As an industry leader in digitalization and implementation of value-added data-driven methodologies, Dow is executing a structured evaluation of predictive maintenance (PdM) vendor offerings. PdM offers a tailored alternative to scheduled maintenance or run-to-failure operations, but the identification of suitable solutions offered by third parties is not trivial given the large number of offerings. This paper describes a methodology developed by Dow to deal with the challenge of efficiently screening many vendors with relevant PdM offerings. Prior to the evaluation process, scoring criteria for vendor performance must be identified. For Dow, these included the requirements (1) models can be created and deployed easily, (2) modeled asset health provides information for root causes, (3) the software operates in our preferred IT architecture, (4) confidential data cannot leave the premises, and (5) models have some transparency. The process involves four steps beginning with vendor identification, which explored existing relationships and landscape surveys. Following was the completion of a questionnaire by vendors about the offering. Upon positive completion, a dataset for two reflux pumps was provided for a first demonstration of the tool. The model performance was compared to internal modeling efforts, of which key results are shared in this paper. The last step involved an in-depth evaluation including on-site installation and online deployment of the PdM models, allowing scoring of all categories. What is presented herein is a framework that can be utilized for screening predictive maintenance modeling tools as well as many analytics applications arising in the age of Industry 4.0.

Forced convection heat transfer in rectangular channels is enhanced by aeroelastically fluttering thin reeds extending over the channel span. The resulting small-scale vortical motions substantially increase local heat transfer at the channel walls and mixing between the wall thermal boundary layers and the channel's core flow. Mechanisms associated with evolution of these small-scale motions and their thermal effects are experimentally studied in a channel of width W, span , and length . Reed effects on heat transfer are characterized at Reynolds numbers (Re) of 2000, 7000, and 12,000 using embedded thermocouple arrays, and related to small-scale motions by particle image velocimetry and hot-wire anemometry. Reed-induced small-scale motions increase turbulent kinetic energy, increasing the global Nusselt number (by up to 145% at Re = 7000), with enhancement being sustained even when the base flow becomes fully turbulent (Re = 12,000). Enhancement is also demonstrated for fin arrays. Single-reed computations show the effect of reed length on enhancement. Computations with two reeds, one downstream of the trailing edge of the other, predict heat transfer enhancement significantly greater than twice the single-reed result, and point the way to use of a streamwise array of reeds in long channels. A techno-economic analysis for an air-cooled condenser suggests that fluttering reeds can be economically justified for a range of operating conditions.

The effective shaping of adsorbents is essential to guarantee a high-efficiency energetic process in what concerns adsorptive applications. In this way, additive manufacturing is gaining significance in developing structured materials in the field of gas adsorption processes. Additive manufacturing is a promising alternative to the traditional shaping techniques, which possess some drawbacks. This paper aims to review the state-of-art of additive manufacturing technologies in the manufacturing of adsorbent structures. To do so, an overview of the existent shaping techniques is done, followed by a summarized description of the leading existing additive manufacturing technologies. Then, the application of additive manufacturing technologies to the development of adsorbent materials and other materials for chemical engineering-related applications is also reviewed.

Four-dimensional (4D) printing, which combines a three-dimensional (3D) printing process with dynamic modulation of materials, provides a possible solution to achieve stimuli-responsive microstructure that exhibit more complex functionality. One of the potential applications for these stimuli-responsive hydrogels is switchable passive radiative cooling and warming. However, most current attempts are limited to mono-functionality, either radiative cooling or warming only. In this article, we demonstrate a double-side morphing flower structure for a dual functionality, switchable cooling or warming dependent on the environment. The morphing structure was designed with the aid of emissivity calculations and then fabricated by the 3D printing method. The transition between open-flower and close-flower states occurs at 32°C within a minute, resulting in ~35% emissivity difference between these two states. As a result, the power difference between inner layer and outer layer was as high as 22 W/m² which is a function of pyramid bottom size.

Cell therapies have the potential to effectively treat and even cure complex, currently untreatable diseases with unprecedented success. The cell therapy industry has been growing rapidly since the Food and Drug Administration approval of the first product in 2017. Despite tremendous promise, there are significant and unique challenges that must be overcome to make cell therapy manufacturing reproducible, scalable, high-quality, and cost-effective. Discovery and implementation of critical quality attributes (CQAs) and critical process parameters (CPPs) to the complex cell therapy manufacturing processes is one such grand challenge for the field. The role of process analytical technologies (PATs) in CQA/CPP discovery and eventual in-process, or at-process monitoring to maintain consistent process and product quality, is indispensable. Here we discuss the major challenges and the strategic framework for optimizing process development and related PATs for various cell therapies, with a focus on upstream processes. We introduce relevant approaches, such as quality-by-design (QbD), and the implementation of PATs to enable QbD in current biomanufacturing processes. We examine state-of-the-art PAT implementation on standard physicochemical parameters in biopharmaceutical operations and consider potential cell therapy-related parameters that may be instrumental in overcoming the challenges of the current cell therapy manufacturing landscape. Current innovations applied to the field, such as high-throughput and high-dimensional analyses, machine learning, and novel sensor technologies, are also discussed. We conclude that advances in PATs are necessary to identify CQAs and CPPs, overcome limitations in current operating processes, reduce overall product cost, and significantly accelerate the translation of laboratory discoveries into commercialized cell therapy products.

Black liquor (BL) dewatering by multi-effect evaporation in the kraft papermaking process is highly energy-intensive. It was previously shown that graphene oxide (GO) nanofiltration membranes can remove lignin, other organics, and inorganic salts from BL while exhibiting stability in caustic BL conditions. Here, we design and simulate several candidate dewatering processes and evaluate their technoeconomic characteristics. All processes concentrate BL from 15 to 30 wt% solutes while producing aqueous permeate. Two process options were analyzed—option A including “last-mile” permeate treatment to reduce solutes to 0.2 wt%, and option B excluding this treatment and producing a 3–4 wt% solutes stream. These processes were simulated in custom-built ASPEN Plus flowsheets interfaced with Microsoft Excel and MATLAB. All processes deliver large (>40%) energy savings. Detailed technoeconomic analysis showed that option A processes are profitable in mills equipped with condensing turbines, but unprofitable with only purchased fuel savings. Option B processes are profitable in both situations, but require the caustic permeate to be utilized in other kraft process units. They are also profitable with electricity generation when operated at smaller scales matching the requirements of other process units. Monte-Carlo sensitivity analysis shows that Option A can yield median 20-year NPVs up to ~$10MM and Option B up to ~$25MM. Overall, GO membrane-based BL dewatering is economically promising, assuming successful slipstream piloting and scale-up campaigns. It would have immediate sustainability benefits from large energy savings, and broader implications for biorefinery processes due to the ability to fractionate biomass feedstock components under harsh conditions.

Many model-based optimization strategies for simulated moving bed (SMB) processes have been proposed to determine operating conditions efficiently; however, there has not been a technique to utilize operation data to enhance the reliability of a mathematical model. This study focuses on developing a parameter estimation method for SMBs to obtain a reliable model utilizing the operation data collected in SMB plants through daily runs, which may contain measurement errors. To use such data, Tikhonov regularization was employed where the regularization parameter is determined by the framework of the discrepancy principle.

The potential of our estimation method has been demonstrated by sugar separation described by a nonlinear isotherm. The parameter estimation was conducted with 20 operation data sets from an SMB pilot plant. The prediction of the resulting model was validated against test data sets, and the confidence regions of the parameters were evaluated. These tests confirmed that the model has improved compared with the model initially obtained.

The beneficial synergies of chemical process intensification and plant modularization present a unique step-wise advancement opportunity for many chemical manufacturers, but economic case studies are needed to raise awareness of such opportunities. The primary objective of this case study is to better understand the business case economics of a specialty chemical plant using modular chemical process intensification (MCPI), by comparing it with that of a conventional stick-built (CSB) plant that produces the same product at the same production capacity. When comparing MCPI against CSB approaches for a plant project strategy decision, analysts should thoroughly understand and model the differences and similarities in scope bases. The MCPI approach for this case study benefitted from dramatic reductions in capital expenditures (CAPEX). A sizeable reduction in plant spatial volume likely explains some of these reductions. Sizeable operational expenditure (OPEX) reductions with the MCPI plant appear to be associated with the reduced operator staffing required from converting a labor-intensive batch process into an automated, continuous flow process. Traditional project investment economic measures strongly favored the MCPI case for the design, construction, and operation of the specialty chemical plant. The net present value for the MCPI case was nearly twice that for the CSB case over a ten-year period, and the payback period for the CSB case was nearly five times longer than that of the MCPI case. The opinion-based perspective of the study participant identified significant contributors to superior MCPI economic performance for this case study. With the two conditions of low MCPI CAPEX and high product profit margin, economic analysis indicates that incorporation of a backup MCPI train, for operational redundancy when downtime occurs, is a very beneficial strategy.

This study focuses on developing continuous reactor network models able to produce multiple rigid polyol products under strict product and safety specifications. We first determine reactor networks and operating decisions for minimum capital cost, and then find reactor networks with optimal profit through multi-criteria optimization, using a Pareto chart to analyze the relationship between capital cost and net sales. Based on our previous study, we narrow down the types of continuous reactors that can be part of the network to two: plug flow reactor (PFR) with multiple feed injection points and a network of continuous stirred tank reactors (CSTR). The CSTR model can be written as a mixed integer nonlinear programming (MINLP) problem. The PFR model is a differential algebraic equation (DAE) optimization problem. The simultaneous collocation method is applied to transform the resulting DAE model into an MINLP, which is solved as a NLP by fixing the binary terms. Both capital cost and multi-criteria problems are formulated as multi-scenario optimization problems to determine the best single network design to produce multiple polymer products.