Triggers for chemical product design: A systematic literature review

The product design project is a complex problem because objectives and constraints must be considered simultaneously, the sustainability context is highly relevant and specific, decision-making involves not only customer needs but also of other stakeholders, especially the organization in which the design project takes place. This work presents a systematic literature review of design methodologies for chemical products to identify how that problem has been addressed and which are the future challenges to be met. The review involved the analysis of 262 research papers and 336 patents, classified according to the chemical product type, the design phase studied, and whether they consider association with a business context. The study highlights the need for holistic product design methodologies applicable from the early design stages, covering the assessment of customer needs and the requirements of other stakeholders, as well as the business context where the design process is carried out.     

Product design: Formulation of a screen-printable sintering-type conductive paste

Due to the complexity of the screen-printing operation and the rheological behaviors of the screen-printable paste, such a paste is usually formulated by trial-and-error. In this report, a systematic procedure, based on heuristics and mechanistic models, for the design of a screen-printable paste is developed. The procedure is demonstrated by a case study of the formulation of a conductive paste of copper particles.     

Computer aided product design tool for sustainable product development

A computer aided product design (CAPD) tool is proposed that finds mixtures matching target properties. Genetic algorithm crossover and mutation operators are completed with insertion or deletion operators adapted for side branches. A new substitution operator is devised for cyclic molecules. The mixture fitness is evaluated by a weighted sum of property performances. Molecules are represented by molecular graphs. They are split into molecular fragments which are built from polyatomic groups. Molecules or molecular fragments can be fixed, constrained or left free for building a new molecule. Building blocks are chemical functional groups or bio-sourced synthons. A specific coding of hydrogen-suppressed atoms is devised that can be used with various property estimation models where atom connectivity information is required. Illustration is provided through three case studies to find levulinic, glycerol and bio-based derivatives as substitute for chlorinated paraffin, methyl p-coumarate ester solvent and blanket wash solvent, respectively.     

Graph machine learning for design of high-octane fuels

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO₂ emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.     

Computational design of structured chemical products

In chemical product design, the aim is to formulate a product with desired performance. Ingredients and internal product structure are two key drivers of product performance with direct impact on the mechanical, electrical, and thermal properties. Thus, there is a keen interest in elucidating the dependence of product performance on ingredients, structure, and the manufacturing process to form the structure. Design of product structure, particularly microstructure, is an intrinsically complex problem that involves different phases of different physicochemical properties, mass fraction, morphology, size distribution, and interconnectivity. Recently, computational methods have emerged that assist systematic microstructure quantification and prediction. The objective of this paper is to review these computational methods and to show how these methods as well as other developments in product design can work seamlessly in a proposed performance, ingredients, structure, and manufacturing process framework for the design of structured chemical products. It begins with the desired target properties and key ingredients. This is followed by computation for microstructure and then selection of processing steps to realize this microstructure. The framework is illustrated with the design of nanodielectric and die attach adhesive products.     

Challenges and opportunities in computer-aided molecular design

In this paper, the significant development, current challenges and future opportunities in the field of chemical product design using computer-aided molecular design (CAMD) tools are highlighted. With the gaining of focus on the design of novel and improved chemical products, the traditional heuristic based approaches may not be effective in designing optimal products. This leads to the vast development and application of CAMD tools, which are methods that combine property prediction models with computer-assisted search in the design of various chemical products. The introduction and development of different classes of property prediction methods in the overall product design process is discussed. The exploration and application of CAMD tools in numerous single component product designs, mixture design, and later in the integrated process-product design are reviewed in this paper. Difficulties and possible future extension of CAMD are then discussed in detail. The highlighted challenges and opportunities are mainly about the needs for exploration and development of property models, suitable design scale and computational effort as well as sustainable chemical product design framework. In order to produce a chemical product in a sustainable way, the role of each level in a chemical product design enterprise hierarchy is discussed. In addition to process parameters and product quality, environment, health and safety performance are required to be considered in shaping a sustainable chemical product design framework. On top of these, recent developments and opportunities in the design of ionic liquids using molecular design techniques have been discussed.     

化学产品过程开发实验平台--公斤实验室的建设和应用

以化工产品设计和过程开发为导向,建立了一个化工产品过程开发实验平台--化工产品公斤实验室Kilo-lab.该公斤规模试验装置可进行反应、结晶、分离、提纯等系列产品的开发过程,研究了流程装置集成和柔性生产系统的规律和特征,为小批量、多品种、高附加值化学产品快速响应市场的变化和需求,以及过程开发的工业化放大提供理论和实验依据.     

自动地面清洗机的开发设计

自动地面清洗机是集喷液、洗地、清洁、吸水、吸尘为一体的机电一体化产品,它是在吸尘器的基础上,经研究而发展起来的另一类新型地面清洗机。自动地面清洗机通过用洗涤液对地面进行清洗,并将污水吸干,达到清洁地面的目的,本文主要介绍自动地面清洗机的工作原理及设计要点。     

Perovskite-based electrocatalyst discovery and design using word embeddings from retrained SciBERT language model

With the ever-increasing volume of scientific literature, there is a strong need to develop methods that allow rigorous information identification. In this contribution, a state-of-the-art natural language processing (NLP) model was used to select perovskite materials for electrocatalytic applications from literature. This was accomplished by obtaining word embeddings for perovskite materials from the NLP model and subsequently designing downstream tasks to discover perovskite-based electrocatalyst materials. However, embeddings could be obtained only for materials available in the literature. Consequently, a novel methodology was devised to generate embeddings for newly designed materials. Results from the analysis showed that the computed embeddings could be used to rank materials for their suitability for electrocatalytic applications. Further, the word embeddings were also employed as features in predicting the electrocatalytic activity of perovskite-based electrocatalysts. The analysis demonstrated that the fidelity of regression models increased when the embeddings were used as features.     

Automated synthesis of steady-state continuous processes using reinforcement learning

Automated flowsheet synthesis is an important field in computer-aided process engineering.The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design.The environment consists of a steady-state flowsheet simulator that contains all physical knowledge.An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem.A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem.Therein,flowsheet synthesis is modelled as a game of two competing players.The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning.The method is applied successfully to a reaction-distillation process in a quaternary system.     

Kinetic study in an automatic continuous-flow photochemical platform with machine learning

In this work, we first solved the partial differential equation of the one-dimensional axial diffusion model in an open-source platform, that is, FEniCS, to explore the influence of the axial dispersion on the reaction yield-to-time profile. Then, we built an automatic platform, which included a photomicroreactor, a continuously controlled pump, a high-power UV-LED light source, an in-line visible-light absorbance analytical unit, and a Raspberry Pi based controlling unit. Moreover, steady-state feeding and sampling functions could be realized in this continuous-flow photochemical platform. The homogeneous photolysis of methylene blue and the photo-Favorskii rearrangement synthesis of ibuprofen as the model reactions were used to validate the robustness of this automatic platform with unsteady-state and steady-state operations, through which kinetic study was carried out using genetic algorithm based symbolic regression, leading to deep understanding on reaction mechanisms and benefits for process optimization.     

Machine learning enabled integrated formulation and process design framework for a pharmaceutical 3D printing platform

The pharmaceutical manufacturing sector needs to rapidly evolve to absorb the next wave of disruptive industrial innovations—Industry 4.0. This involves incorporating technologies like artificial intelligence and 3D printing (3DP) to automate and personalize the drug production processes. This study aims to build a formulation and process design (FPD) framework for a pharmaceutical 3DP platform that recommends operating (formulation and process) conditions at which consistent drop printing can be obtained. The platform used in this study is a displacement-based drop-on-demand 3D printer that manufactures dosages by additively depositing the drug formulation as droplets on a substrate. The FPD framework is built in two parts: the first part involves building a machine learning model to simulate the forward problem—predicting printer operation for given operating conditions and the second part seeks to solve and experimentally validate the inverse problem—predicting operating conditions that can yield desired printer operation.     

螺杆设计方法探讨及在果筐注射机螺杆设计中的应用

介绍了作者设计果筐注射机专用波状螺杆的心得,以此说明设计螺杆的一种思路.借助于相似理论,给出了不同型号果筐注射机专用波状螺杆的设计准则.     

基质修护系统和护肤品设计思路

皮肤由细胞和基质构成,基质是存在于细胞周围,由细胞自身合成、分泌的生物活性物质,不同的皮肤层面,基质的组成不同。基质的组成和结构处于良好状态,皮肤细胞所需要的水分、营养和代谢废物就会得到及时调运和周转,细胞的活力就强。皮肤在保护人体的同时,自身难免要受到伤害,皮肤在长期进化过程中,演化出一整套基质自我修护机制。护肤品应该根据皮肤自身基质修护系统作用机制进行设计,强化皮肤自身修护的同时不给皮肤带来负担。     

Product design: A pricing framework accounting for product quality and consumer awareness

A systematic framework has been developed to determine the optimal price of a completely new (or existing but improved) chemical product that is being launched (or relaunched) in the presence of a competing product. It has four elements. The first is a pricing model derived from a utility function with constant elasticity of substitution. It accounts for consumers' awareness of the product under consideration and consumer preferences. The second is a set of relationships relating the consumer preferences and the relevant sales data available to a relaunched product to the parameters of the pricing model. In the absence of sales data for a completely new product, the third element is a set of heuristics for choosing a pricing strategy and estimates of the pricing model parameters. The optimal price is finally determined in a profit maximization problem subject to the market size as well as any other constraints. This pricing framework allows simultaneous optimization of product quality and price using product specifications as design variables. It is illustrated with an example on energy drinks. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2462–2471, 2018     

Automated process design of acid gas removal units in natural gas processing

This study looks at the design of the acid gas removal unit (AGRU) for natural gas processing. For the purpose of enhancing energy efficiency a number of different structural options are considered including multiple feeds, semi-lean and pump around modifications in addition to modification of operational parameters. Previous studies in this area have considered the comparison of different individual configurations but there has been a lack of research considering the simultaneous optimization of equipment configuration. Hence, in this study a superstructure-based optimization approach is used to simultaneously identify the most appropriate arrangement and operating conditions while the maximum energy recovery potential is also realized with the aid of energy composite curves (ECC). This methodology is applied to a case study where it is shown that the optimal configuration contains a combination of pump around and semi-lean process modifications allowing a 15.9% reduction of utility costs.     

论景德镇日用陶瓷产品形态开发

从景德镇日用陶瓷产品的现状出发,针对日用陶瓷形态开发中的相关问题进行研究探讨,提出了日用陶瓷形态开发的具体方法,从材料、技术和设计三个方面,对日用陶瓷形态开发提出了一些见解,为未来景德镇日用陶瓷创新发展提供一条新路。