Product Design and Engineering in Chemical Engineering: Past, Present State, and Future

Uhlemann, Jens; Costa, Raquel; Charpentier, Jean‐Claude

doi:10.1002/ceat.201900236

Cited by 12 publications

(13 citation statements)

References 152 publications

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“…Figure 5 shows the cumulated mentions of each product type (including mentions of those documents which refer to more than one type of product): formulated (126), molecules (78), commodities (19), and devices (9). It is worth noting that in the evolution by product type, there is a marked interest in formulated products for the perspectives of product design in chemical engineering, as remarked by some authors 2,9 …”

Section: Resultsmentioning

confidence: 99%

Triggers for chemical product design: A systematic literature review

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

Triggers for chemical product design: A systematic literature review

et al. 2022

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“…Common mathematical complexities encountered in CPE problems are non-linearity, large and multi-scale systems, long dynamics, uncertainties and high-dimensionality [52,53]. When there is, additionally, a lack of sufficient knowledge about the physical and chemical laws governing the system, it becomes very difficult and time-consuming to develop pure physico-chemical (i.e., knowledge-based) models to solve these problems.…”

Section: Hybrid and Combinatorial Approachesmentioning

confidence: 99%

“…In particular, the interest in these high value-added chemical products has become even more marked due to the competitive worldwide context and dynamic market demands. Chemical industries have to differentiate themselves by constantly developing innovative products as fast and as economically as possible while ensuring quality, performance and sustainable manufacturing [53,[72][73][74][75].…”

Section: In Chemical Productmentioning

confidence: 99%

“…On the one hand, chemical engineering elaborates commodity chemicals, well known and best served by process design with a focus on the optimal and sustainable transformation of raw materials and energy into targeted products. On the other hand, the problems encountered in CPE aim at developing new and/or improved products based on customer needs and/or new technologies [53,76].…”

Section: In Chemical Productmentioning

confidence: 99%

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Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

2021

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Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer.

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“…The concept of Pharmaceutical Materials Science in the pharmaceutical community has strong inherent similarities to the concept of product design and engineering (PDE) in the chemical engineering community. PDE is concerned with the definition of new and/or improved products based on the inputs of customer needs and/or new technologies [ 38 , 41 ]. PDE is the chemical engineering contribution to the new product development (NPD) workflow in the industrial sector such as the pharmaceutical industry.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations—A Particle Technology Perspective

Uhlemann

Diedam

Hoheisel³

et al. 2020

Pharmaceutics

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Crystalline organic nanoparticles and their amorphous equivalents (ONP) have the potential to become a next-generation formulation technology for dissolution-rate limited biopharmaceutical classification system (BCS) class IIa molecules if the following requisites are met: (i) a quantitative understanding of the bioavailability enhancement benefit versus established formulation technologies and a reliable track record of successful case studies are available; (ii) efficient experimentation workflows with a minimum amount of active ingredient and a high degree of digitalization via, e.g., automation and computer-based experimentation planning are implemented; (iii) the scalability of the nanoparticle-based oral delivery formulation technology from the lab to manufacturing is ensured. Modeling and simulation approaches informed by the pharmaceutical material science paradigm can help to meet these requisites, especially if the entire value chain from formulation to oral delivery is covered. Any comprehensive digitalization of drug formulation requires combining pharmaceutical materials science with the adequate formulation and process technologies on the one hand and quantitative pharmacokinetics and drug administration dynamics in the human body on the other hand. Models for the technical realization of the drug production and the distribution of the pharmaceutical compound in the human body are coupled via the central objective, namely bioavailability. The underlying challenges can only be addressed by hierarchical approaches for property and process design. The tools for multiscale modeling of the here-considered particle processes (e.g., by coupled computational fluid dynamics, population balance models, Noyes–Whitney dissolution kinetics) and physiologically based absorption modeling are available. Significant advances are being made in enhancing the bioavailability of hydrophobic compounds by applying innovative solutions. As examples, the predictive modeling of anti-solvent precipitation is presented, and options for the model development of comminution processes are discussed.

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Product Design and Engineering in Chemical Engineering: Past, Present State, and Future

Cited by 12 publications

References 152 publications

Triggers for chemical product design: A systematic literature review

Triggers for chemical product design: A systematic literature review

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations—A Particle Technology Perspective

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