Journal of advanced manufacturing and processing最新文献

In the present study, a series of thermochemical equilibrium modeling was conducted to assess the thermodynamic potential of biomass conversion to ammonia using thermal and nonthermal plasma at small- and large-scale production. The system was designed and evaluated for five different locations in Australia including the Northern Territory, South Australia, Western Australia, and New South Wales using local biomass feedstock. The equilibrium modeling showed that the pathway of biomass to biomethane using an anaerobic digestion reactor, biomethane to hydrogen using a thermal plasma reactor, followed by conversion of hydrogen to ammonia via a nonthermal plasma reactor is a plausible route, by which the exergy efficiency of the process can be as high as ~60%. It is identified that the thermal plasma reactor required two distinct zones at 3000°C < T < 4000°C and 1500°C < T < 2500°C. The first zone aims at converting electric energy into very high temperature thermal flow while the second one enables to split methane molecules into solid carbon and hydrogen. The new ammonia process is also assessed from the viewpoint of the current industrial transformation, being accelerated by the post-COVID economy, which moves toward local, resilient, integrated and self-sufficient production under the umbrella of an emerging fractal economy. With respect to local production, the developed process is designed for a quick response to farm use and on-time production in view of the demands of modern ICT-sensor based precision agriculture. The proposed process was found to be flexible (“resilient”) against production scale, geographical location, price and type of feedstock, and source of renewable energy. The system was found to be flexible against different feedstock such as spent grape marc, mustard seed, bagasse, piggery and poultry. The system can be self-sustained up to ~80% at T = 3500°C; with the thermal plasma reactor-zone 2 producing the electricity requirements for the nonthermal plasma via a steam turbine power block. Finally, the system it is investigated to which degree the system is adaptable to local production, self-sufficient, and circulatory.

Due to economic, social and technical trends, as well as rising energy costs, the food and beverage industry is challenged to work energy-efficiently. Small and medium-sized enterprises in particular lack of both time and knowledge to identify and implement suitable energy efficiency measures. With the help of simulation, decision-makers can pursue numerous approaches and make well-founded decisions. A metamodel based on four modeling pillars (physical-, process-, article/recipe-, and production plan model) for an entire production system concerning the forecast of energy and media consumption is presented. The metamodel is implemented in a user-friendly modeling tool, which enables a simple hybrid modeling without prequalification of the user. Subsequently, a standardized data exchange file, as basis for a simulation model, is generated. For validation, various use cases were modeled and the tool was validated using a systematic test. In addition, two simulation studies were performed to show that the presented approach provides the opportunity to create a holistic model in terms of forecasting energy and media consumption.

Many chemical manufacturing and separations processes like solvent extraction comprise hierarchically complex configurations of functional process units. With increasing complexity, strategies that rely on heuristics become less reliable for design optimization. In this study, we explore deep reinforcement learning for mapping the space of feasible designs to find an optimization strategy that can match or exceed the performance of conventional optimization. To this end, we implement a highly configurable learning environment for the solvent design process to which we can couple state-of-the-art deep reinforcement learning agents. We evaluate the trained agents against the heuristic optimization for the solvent process design tasked to optimize recovery efficiency and product purity. Results demonstrated the agent successfully learned the strategy for predicting comparably optimal solvent extraction process designs for varying combinations of feed compositions.

Sustainable production is essential for the future of the global economy. Despite the publication of its baseline vision over 30 years ago and the resulting diversity of interpretations and subdisciplines in engineering and social sciences, the progress of the approach in industrial practice remains marginal. This is mainly due to the fact that the discipline has not yet succeeded to realize the magnitude of the rethinking necessary of its very own perception as a whole. Existing definitions of sustainable production presented to date are thus only partly consistently derived from the baseline concept. Meanwhile, digitalization provides an increasing number of technologies that offer a new perspective on sustainable production. This especially applies to the concept of digital twins. Recent studies, thus, address their role in the context of sustainable production by analyzing its contribution to existing sustainability related methods as well as technical challenges on a microeconomic level (bottom-up approach). Although these approaches provide concrete requirements for technical deployment, it is highly questionable how they will contribute to sustainable production as a whole. In this paper, we choose a top-down approach to discuss the role of digital twins in the context of sustainable production. Based on fundamental reflections on the baseline concept of sustainability, we advocate a reorientation of production within the framework of planetary boundaries. Thereupon, we discuss the role of digital twins and digital threads and provide a number of requirements that future R&D needs to address for a future sustainability-oriented data-driven monitoring and regulation of production.

In a typical Haber-Bosch process, a gas stream containing 15–20 mol% ammonia is obtained from the reactor effluent, and ammonia is then partially separated in a phase-changing condensation unit. When operating at lower pressure for distributed manufacturing, the single-pass conversion drops to less than 10 mol%, which makes the condensation more cost-intensive. A small adsorber is proposed for concentrating ammonia through rapid pressure swing adsorption (RPSA) that fits the small-scale processing. A mathematical model is developed to evaluate the feasibility of the RPSA process for ammonia separation with high recovery. The ideal adsorbed solution theory, based on the Freundlich single-component isotherm model, is proposed to predict binary isotherms for various commercially available adsorbents. The performance of the RPSA-assisted adsorber is then studied at different process conditions for concentrating ammonia. The effect of various operating variables such as exhaust flow rate, cycle time, and feed pressure, is investigated. The proposed numerical model shows that nearly pure ammonia can be continuously produced at optimized conditions, with more than 95% recovery. This low-pressure RPSA-assisted adsorber can be used to design modular ammonia devices for distributed manufacturing. Our proposed technology can be further extended to concentrate other dilute gas mixtures, such as carbon dioxide.

On the way toward a carbon-neutral economy, a rise in the demand for separation technologies can be expected. This holds especially for membranes, which are energy efficient and thus very promising technologies. However, this challenges membrane researchers to consider the sustainability and scalability of their membrane fabrication processes. At our institute, we employ a roll-to-roll coating process for the production of thin film composite membranes. This procedure is relatively easy to scale up and can be adapted for different polymers/solvents and thus applications. To increase the production efficiency and optimize the process for any polymer of interest, it is necessary to develop a solid understanding of the physics of this production system. Therefore, we would like to present a numerical model based on computational fluid dynamics that can predict the film thickness of a polydimethylsiloxane-based polymer coated in a roll-to-roll setup. In the future, this model should improve the production efficiency and fine-tuning of process parameters. We verify the numerical procedure with a mesh refinement study and validate the predicted film thicknesses with experimental results for different roll speeds and polymer concentrations. The predicted variability of the thickness is assessed by a design of experiments study and compares relatively well to the measured variations of the coated membrane thickness.

AIChE's Advanced Manufacturing and Processing Society held its second annual “Industry 4.0 Digital Transformation Conference” on December 8. The premise of this year's virtual conference was to focus on how COVID-19 has disrupted manufacturing operations and supply chains across the US and globally. This conference explored the role of digital transformation and how that impacted manufacturing during the early days of the pandemic, how manufacturers reacted, and what the future may hold for manufacturing and continued digitalization. These topics were underpinned by a key note talk from 3 M on manufacturing of personal protective equipment (PPE) and a presentation by Sandoz, a Novartis company and how they have embraced the digital transformation in pharmaceutical development. There were two panel sessions that addressed questions about how digital transformation was accelerated during the onset of the pandemic and what the future will hold for ongoing digital transformation. Finally, a digital transformation roadmap discussion and short workshop were held to complete the conference.

A summary of each session follows.

Dr. Cristina Thomas's introduction of 3 M told us about a striking feature of innovation: “Curiosity is just the beginning.” As a major global company, 3 M has sales in nearly every country on the planet. They have recent sales of approximately $32 billion, employ over 96 000 people and have over 120 000 patents. 3-M has four business groups: Safety and Industrial, Transportation and Electronics, Healthcare, and Consumer. Those groups have been working together to address challenges resulting from the pandemic. While most people heard about the challenges of ramping up production for N95 respirators, 3 M also ramped up production of other solutions in response to COVID-19, for example, sanitizers and disinfectants.

One important aspect that 3 M works on is biopharmaceutical filtration. 3 M works with public-private manufacturing institutes like Manufacturing USA's RAPID. The current situation put biopharmaceutical filtration at the center for using the surface modification technology of membranes into speeding up the development of vaccines as well as therapies.

Digital platforms that are in use at 3-M include: Computer vision, Data science, Electronic systems, Modeling & Simulation, Advanced robotics, Sensors, and Software solutions. These digital capabilities augment material science and domain expertise.

The corporate research systems laboratory has five strategic platforms: Internet of Things (IOT), Edge Computing, Artificial Intelligence (AI), Modeling & Simulation, Visualization & Perception, and Data.

Dr. Vijay Rajamini of 3 M provided additional discussion regarding their PPE manufacturing response. There has been a tremendous surge for digital solutions to respiratory PPE challenges during the pandemic. Visualization as well as sharing of knowledge transfer of data and anal

We present a correlation that represents the capital costs of the components of even very small modular facilities. Conversion of distributed feedstocks (eg, associated natural gas, biomass, and carbonaceous wastes) could provide a small fraction of the liquid fuels now used for transportation in the U.S. (~6%), or nearly half of the chemical products now made from petroleum. However, those resources tend to be available at small geographically distributed sites, and they are difficult or expensive to transport to a distant processing center. Modular, and likely intensified, processes that can be numbered up promise to enable utilizing such distributed resources. Early stage economics evaluation of those processes requires cost estimates for the components, which are likely smaller in scale than can be accommodated by the 0.6 power law typically used in chemical engineering.

Chemical devices are chemical products that transform a feed stream into an outlet stream with the desired attributes by performing reaction, fluid flow, heating, cooling, and/or separations. They resemble small chemical plants and can be described by device flowsheets, similar to the process flowsheets of chemical plants. This article proposes a generic design framework for chemical devices. It shares some steps with Douglas' hierarchical design procedure for chemical processes but has its own distinguishing features such as capturing of consumer preferences, formulation of an engineering statement, and the use of unconventional processing techniques in device manufacturing. Two examples, domestic dehumidifier and air purifier, are discussed to illustrate the design framework. A new dehumidifier design was found using this framework.

This whitepaper summarizes the outcome of the first Advanced Process Control (APC) workshop in the pharmaceutical industry, presented by AIChE PD2M, and held in Washington DC, Sep 30 to Oct 01, 2019. Approximately 50 attendees from regulatory agencies, industry and academia had an opportunity to share perspectives and best practices on the business, technical and regulatory aspects of APC for both small and large molecule drug manufacturing. The event consisted of keynote talks, case studies and panel discussions, filled with lively interactions that focused on: (a) Business drivers for APC in pharma; (b) Alignment on the definitions of key terminology; (c) Clarification of roles and relationships of APC with regards to popular initiatives such as Quality by Design (QbD), Process Analytical Technology (PAT), Real Time Release testing (RTRt), Continued Process Verification (CPV), continuous manufacturing and digital manufacturing; (d) APC manufacturing implementation considerations; (e) Quality system and regulatory considerations for APC implementation; (f) APC opportunities in modular manufacturing, process intensification, integrated continuous manufacturing. (g) standards, training, and collaboration.