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Sustainable chemical processes should be designed to combine the technological advantages and progress with lower safety risks and minimization of environmental impact such as, for example, reduction of raw materials, energy and water consumption, and avoidance of hazardous waste and pollution with toxic chemical agents. A number of novel eco-friendly chemical technologies have been developed in the recent decades with the help of the eco-innovations approaches and methods such as Life Cycle Analysis, Green Process Engineering, Process Intensification, Process Design for Sustainability, and others. An emerging approach to the sustainable process design in process engineering builds on the innovative solutions inspired from nature. However, the implementation of the eco-friendly technologies often faces secondary ecological problems. The study postulates that the eco-inventive principles identified in natural systems allow to avoid secondary eco-problems and proposes to apply these principles for sustainable design in chemical process engineering. The research work critically examines how this approach differs from the biomimetics, as it is commonly used for copying natural systems. The application of nature-inspired eco-design principles is illustrated with an example of a sustainable technology for extraction of nickel from pyrophyllite.
The proposed method includes identification and documentation of the elementary TRIZ inventive principles from the TRIZ body of knowledge, extension and enhancement of inventive principles by patents and technologies analysis, avoiding overlapping and redundant principles, classification and adaptation of principles to at least following categories such as working medium, target object, useful action, harmful effect, environment, information, field, substance, time, and space, assignment of the elementary inventive principles to the at least following underlying engineering domains such as universal, design, mechanical, acoustic, thermal, chemical, electromagnetic, intermolecular, biological, and data processing. The method includes classification of abstraction level of the elementary principles, definition of the statistical ranking of principles for different problem types, and specific engineering or non-technical domains, definition of strategies for selection of principles sets with high solution potential for predefined problems, automated semantic transformation of the elementary inventive principles into solution ideas, evaluation of automatically generated ideas and transformation of ideas to innovation or inventive concepts.
As engineering graduates and specialists frequently lack the advanced skills and knowledge required to run eco-innovation systematically, the paper proposes a new learning materials and educational tools in the field of eco-innovation and evaluates the learning experience and outcomes. This programme is aimed at strengthening student’s skills and motivation to identify and creatively overcome secondary eco-contradictions in case if additional environmental problems appear as negative side effects of eco-friendly solutions. The paper evaluates the efficiency of the proposed interdisciplinary tool for systematic eco-innovation including creative semi-automatic knowledge-based idea generation and concept development. It analyses the learning experience and identifies the factors that impact the eco-innovation performance of the students.
The internal crowdsourcing-based ideation within a company can be defined as an involvement of its staff, specialists, managers, and other employees, to propose solution ideas for a pre-defined problem. This paper addresses a question, how many participants of the company-internal ideation process are required to nearly reach the ideation limit for the problems with a finite number of workable solutions. To answer the research question, the author proposes a set of metrics and a non-linear ideation performance function with a positive decreasing slope and ideation limit for the closed-ended problems. Three series of experiments helped to explore relationships between the metric attributes and resulted in a mathematical model which allows companies to predict the productivity metrics of their crowdsourcing ideation activities such as quantity of different ideas and ideation limit as a function of the number of contributors, their average personal creativity and ideation efficiency of a contributors’ group.