Process systems engineering thinking and tools applied to sustainability problems: current landscape and future opportunities

Life cycle optimization oriented to sustainable waste management and circular economy: A review

Life cycle optimization (LCO) is an effective decision-making method combining life cycle assessment and optimization, which is capable of adjusting system configurations to meet specified sustainability goals. This study analyzed the status quo of LCO studies related to sustainable waste management and the circular economy. Most studies have focused on simultaneously optimizing environmental and economic objectives, whereas few have considered social impacts. Greenhouse gas emissions is the most commonly used environmental indicator in optimization, followed by the endpoint single-score indicator. A static deterministic model is often employed to formulate an LCO problem, while uncertainty and dynamic models are less frequently applied but cause concerns. To deal with multi-objective optimization, the ε-constraint method and non-dominated sorting genetic algorithm are popular. Waste LCO has been mainly applied to macro system planning, such as integrated municipal solid waste management systems, biowaste supply chains, waste-to-energy systems, and waste-to-resource networks, aiming to determine optimal waste allocation, facility capacity/location, technology choice, etc. It is occasionally used in optimizing process structure, operating conditions, blending ratio of feedstocks, and product development. Future research should focus on exploring the integration of more environmental and social indicators into multi-objective optimization, modeling under uncertainty, dynamic LCO, process and product optimization, and addressing the lack of multi-scale studies.

The P-graph approach in optimal synthesis and planning of waste management towards achieving sustainable development goals: A systematic review

Effective waste management remains a challenge in global environmental sustainability, underlining the urgent necessity for innovative solutions. This review explored waste management strategies, focusing on the role of P-graph frameworks in achieving sustainable development goals (SDGs). P-graphs offer a systematic approach across domains including, chemical reaction routes, carbon management networks, economic systems and resource planning to waste management synthesis and planning. Through a systematic search and analysis of relevant P-graph approaches, 28 articles meeting the inclusion criteria were selected for review. The study reveals that P-graph approach is a systematic methodology that can streamline decision-making processes, which ultimately lead to more efficient and effective waste management strategies and solutions. This research also highlighted the absence of previous studies on the application of the P-graph approach to various types of waste, underscoring its significance and originality in the field. This study seeks to advance the achievement of SDGs and promote sustainable waste management practices through the integration of the P-graph framework with waste management solutions.

Big data analytics opportunities for applications in process engineering

Big data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. Initially, an overview of big data content, key characteristics, and related topics are presented. The paper also highlights a systematic review of available big data techniques and analytics. The available big data analytics tools and platforms are categorized. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data.

Screening biorefinery pathways to biodiesel, green-diesel and propylene-glycol: A hierarchical sustainability assessment of process

Plant design implies the best choice among a set of feedstock-to-product process pathways. Multiple sustainability performance indicators can blur the decision, and existing sustainability assessment methods usually focus only on environmental life-cycle performance and corporate metrics or solely on the gate-to-gate process. It is relevant to incorporate integrated system analysis to address sustainability comprehensively. To this end, the Sustainable Process Systems Engineering (S-PSE) method was previously introduced to select the most sustainable feedstock-process-product configuration via four-dimensional indicators (environment, efficiency, health-&-safety, and economic), and then pinpoint the sustainability hotspots of the best design to unveil possible improvements. This work expands S-PSE by adding new features: (i) cradle-to-gate environmental assessment; (ii) composition of flowsheets; (iii) new indicators; (iv) statistical screening of indicators; and (v) 2030 Agenda compliance. A biorefinery case-study demonstrates S-PSE: to select the best pathway from soybean-oil, palm-oil, and microalgae-oil to biodiesel, green-diesel, and propylene-glycol. Firstly, statistical screening reduces the indicator set by 62%. Results evince all routes from microalgae-oil as economically unfeasible due to oil cost, despite superior environmental performance. S-PSE evinces palm-oil-to-biodiesel as the most sustainable due to lower cradle-to-gate emissions and manufacturing cost, with sustainability hotspots associated to hazardous methanol input and energy-intensive distillations. 2030 Agenda analysis also outlines palm-oil-to-biodiesel as best for 5 out of 10 Sustainable Development Goals linked to the reduced indicator set.

Process modelling and life cycle assessment coupled with experimental work to shape the future sustainable production of chemicals and fuels

Meeting the sustainable development goals and carbon neutrality targets requires transitioning to cleaner products, which poses significant challenges to the future chemical industry. Identifying alternative pathways to cover the growing demand for chemicals and fuels in a more sustainable manner calls for close collaborative programs between experimental and computational groups as well as new tools to support these joint endeavours. In this broad context, we here review the role of process systems engineering tools in assessing and optimising alternative chemical production patterns based on renewable resources, including renewable carbon and energy. The focus is on the use of process modelling and optimisation combined with life cycle assessment methodologies and network analysis to underpin experiments and generate insight into how the chemical industry could optimally deliver chemicals and fuels with a lower environmental footprint. We identify the main gaps in the literature and provide directions for future work, highlighting the role of PSE concepts and tools in guiding the future transition and complementing experimental studies more effectively.

Process assessment, integration and optimisation: The path towards cleaner production

This contribution starts from the broad perspective of the global material cycles, analysing the main resource and pollution issues world-wide from the viewpoint of the disturbances to these cycles caused by human activities. The issues are analysed in the light of the currently developing COVID-19 pandemic with the resulting behavioural and business pattern changes. It has been revealed in the analysis of previous reviews that there is a need for a more comprehensive analysis of the resource and environmental impact contributions by industrial and urban processes, as well as product supply chains. The review discusses the recent key developments in the areas of Process Integration and Optimisation, the assessment and reduction of process environmental impacts, waste management and integration, green technologies. That is accompanied by a review of the papers in the current Virtual Special Issue of the Journal of Cleaner Production which is dedicated to the extended articles developed on the basis of the papers presented at the 22nd Conference on Process Integration for Energy Saving and Pollution Reduction. The follow-up analysis reveals significant advances in the efficiency and emission cleaning effects of key processes, as well as water/wastewater management and energy storage. The further analysis of the developments identifies several key areas for further research and development - including increases of the safety and robustness of supply networks for products and services, increase of the resources use efficiency of core production and resource conversion processes, as well as the emphasis on improved product and process design for minimising product wastage.