Optimisation and process design tools for cleaner production

Journal Pre-proof Optimisation and Process Design Tools for Cleaner Production

Yee Van Fan, Hon Huin Chin, Jiří Jaromír Klemeš, Petar Sabev Varbanov, Xia Liu PII:

S0959-6526(19)34051-X

DOI:

https://doi.org/10.1016/j.jclepro.2019.119181

Reference:

JCLP 119181

To appear in:

Journal of Cleaner Production

Received Date:

26 October 2019

Accepted Date:

04 November 2019

Please cite this article as: Yee Van Fan, Hon Huin Chin, Jiří Jaromír Klemeš, Petar Sabev Varbanov, Xia Liu, Optimisation and Process Design Tools for Cleaner Production, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.119181

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Journal Pre-proof Optimisation and Process Design Tools for Cleaner Production Yee Van Fana, Hon Huin China, Jiří Jaromír Klemeša, Petar Sabev Varbanova, Xia Liub aSustainable

Process Integration Laboratory – SPIL, NETME Centre, Faculty of Mechanical

Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno Czech Republic. bSINOPEC

Shanghai Research Institute of Petrochemical Technology, Shanghai, 201208,

China Abstract: Assessments of hotspot analysis and process optimisation followed by improved design are essential to achieve cleaner production. Cleaner production also involves complex interactions with economic and social performance. It plays a substantial role in sustainable development. This contribution presents an overview of cleaner production achievements and selection of relevant recent work dealing with optimisation tools and process design as published in the Special Issue on Process Integration and Intensification for Sustainable Evolution via Resource and Emission Reduction. The cleaner production tools including Pinch Analysis, Process Graph, Artificial Intelligence and computer-aided modelling, are reviewed. The roles of waste streams as secondary resources process design in cleaner production and circular economy is also discussed. The highlights of the recent development contribute to the field of study by drawing out the attention for potential future research. 1.0

Introduction Cleaner production stresses on the inauguration of the best environmental practice in

manufacturing and operational processes. It can be defined as a process of continuous improvement that aims at the efficient use of natural resources avoiding environmental impacts of processes, product or services, generating economic benefits and organisation change (Vieira and Amaral, 2016). The concept is similar to Best Available Techniques - BAT (EEC, 1984) as process modification aims to minimise emission created by the manufacturing operations through raw material, water and energy consumption reduction as well as minimising the emission and hazardous waste generation (Yilmaz et al., 2015). There are established reference documents for BAT where one of the latest version is dedicated to the management of waste from extractive industries (Garbarino et al., 2018). Figure 1 shows the

Journal Pre-proof general idea of cleaner production where input/the use of resources has to be minimised or replaced by cleaner sources in generating maximum output with minimum by-products. Inappropriate decision making could lead to the cross-media effects (transfer of burdens from one medium to another problem) or shift of environmental footprints, e.g. low GHG footprint but high in water footprint or total environmental burden (Fan et al., 2019). The limited sphere of thinking that focus only on the environmental aspect could lead to economic and social impacts. The compromise between different objective functions for sustainable system design (see Figure 1) has always been the on-going research and assessment. Waste treatment is included as part of cleaner production in some of the definition (Vieira and Amaral, 2016). However, in some case, it is considered as a separated downstream strategy.

Figure 1: Cleaner production and sustainable development. Three pillars of sustainability diagram are adapted from Von Keyserlingk et al. (2013) Environmental impacts and environmental footprints are indicators representing environmental sustainability in optimising the processes. GHG including Carbon Emissions Footprints and Global Warming Potential are the most common index for cleaner production analysis due to the growing attention on climate change issues as well as the policy driver. Figure 2a shows the share of CO2 emission by industry. Iron and steel are the most significant contributor which required higher awareness on cleaner production practice followed by cement, chemicals, plastic, paper and aluminium. In term of CH4, GMI (2019) estimated that 27 % of the source in 2020 is from enteric fermentation, as in Figure 2b. Agriculture, oil and gas, coal mining and waste (both wastewater and municipal solid waste) representing the potential area for methane mitigation.

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(a) (b) Figure 2: Share of emissions a) global CO2 emissions of industries, adapted from Financial time (2019), b) Estimated global anthropogenic CH4 by the source in 2020, adapted from GMI (2019). Based on the statistics by sectors, energy and transportation are the main contributors (EIA, 2017) of GHG emissions. Replacing the utility (e.g. renewable energy) and feedstock (e.g. bio-based, waste as resources) with a cleaner alternative is one of the highly studied strategies to reduce the environmental footprints for cleaner production. Figure 3 shows the environmental footprints of different energy sources. By referring to the climate impact, renewable energy including photovoltaic, concentrated solar, wind and hydropower are substantially cleaner than the coal and natural gas. However, renewable energy has a higher impact in term of material and land use. This suggests the potential pitfall of defining cleaner production or environmental sustainability by limiting to climate change or global warming impact.

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Figure 3: Life cycle assessment of different energy generation approaches. See Hertwich et al. (2016) for the baseline scenario in comparison, reported in % (logarithm scale). There has been an increasing number of studies assessing the nexus between two or more footprints for a better understanding of environmental sustainability. For example, Aguilera et al. (2019) assessed the climate-food-energy-water nexus of irrigation. Schmidt and Matthews (2018) conducted a similar nexus assessment with the differences in how the climatefood-energy-water nexus shaped by financial actors are identified. Ringler et al. (2013) studied the nexus across water, energy, land and food in. Figure 4 shows the interaction between the energy and water consumption in the water and energy sectors. The global energy system, whether the energy access, security or the response to climate change, can be exacerbated by changes in water availability (IEA, 2019). Sponge city (Liu et al., 2017) is one of the important proposed urban development to manage urban rainwater effectively. The availability of water can challenge the reliability as well as the overall environmental performance of the existing operation of energy sector (Jia et al., 2019). For water sector, energy production can impact

Journal Pre-proof freshwater availability and quality. Wastewater treatment processes also emit GHG, such as N2O (Thakur and Medhi, 2019) and CH4 (Zhao et a1., 2019). The synergy between different impacts categories has to be considered. The assessment system boundary could also have a considerate effect to the overall sustainability of an approach. For example, Figure 3 has not considered the lifespan of energy generation plant and disposal of end of life products (e.g. batteries, panel, wind blades). Ipsen et al. (2019) applied urban metabolism (metabolic flows) coupled with life cycle assessment to assess the environmental performances of different smart city scenarios. This approach made the burden-shifting from one stage to another transparent as it is quantifiable. A comprehensive accounting methodology (broader boundary, explicit assumptions, economic and social impacts, direct and indirect footprints) assisted by engineering tool is critical to prevent bias in the identified results. As shown in Figure 1, another strategy of cleaner production is enhancing the process efficiency by optimising the key parameters for quality output with a minimum amount of byproducts/waste or by recovery. Process Integration together with process intensification (Klemešs and Varbanov, 2013) has been widely applied in industry to achieve substantial reductions in industrial energy, water and utility use (Klemeš et al., 2018). The relation to the circular integration concept was further introduced by Walmsley et al. (2019). It is a method of taking a holistic approach to process design and optimisation that looks at how a collection of processes or system are best integrated (Linnhoff, 1994), originates from systematic efforts to improve heat recovery in the industry. Process Integration is in line with the circular economy and industrial symbiosis (Yeo et al., 2019) concept, and the resources utilisation is optimised either within a system, e.g. waste heat recovery in industry- Jouhara et al. (2018) and very significant development was Total Site (Klemeš et al., 1977) extended into Local Energy System Integrations by Perry et al. (2008). The implementation of waste heat recovery from data centre repurpose for residential heating was optimised by Antal et al. (2019). Suárez-Eiroa et al. (2019) summarise that the common strategies of the circular economy are minimising inputs of raw materials and outputs of waste, keep resource value as long as possible within the system and reintegrating products into the system at its end of life. Figure 5 shows the waste or post-consumer material of different renewable energy generation approaches including an electric car, solar and wind energy. The materials can be recycled through reprocessing (burdening footprint) or reuse for a lower requirement purpose. For example, car batteries can be used as stationary energy storage. However, these recycling and reuse alternatives possess various challenges as illustrated in Figure 5 as well as incur high implementation cost.

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Figure 4: World Energy and Water sector consumption. IEA (2019)

Figure 5: Waste disposal and treatment of renewable energy generation. Adapted from Bomgardner and Scott (2018) A cleaner production needs to be sustainable where optimisation plays an important role to support decision making. A review by Vieira and Amaral (2016) summarised the tools used to apply cleaner production. It can be classified as: flow analysis tools, quality tools,

Journal Pre-proof accounting analysis tools, TRIZ (the theory of inventive problem solving) tools and other (e.g. brainstorming and lean practices). Gracia et al. (2018) applied the cleaner production tool include flow analysis, ecomap, ecobalance, critical points diagnosis, five year financial analysis etc to identified the cleaner production potential of a medium-sized manufacturing company, The cleaner production tools, however, focus more to the monitoring and assessment framework than decision-making tools for an optimised system design that considering the trade-off of different objective functions. This presented review aims to summarise the recent sustainable strategies and optimising methodologies for cleaner production. It is supported by the published work in the Special Issue (SI) on Process Integration and Intensification for Sustainable Evolution via Resource and Emission Reduction. Cleaner production is a dynamic process where new measures and technologies are continuously emerging as well as being introduced to the industries. A wide range of cleaner production initiatives in the effort to support sustainable development and efficient resources management are considered. The SI benefits from the research presented at the conference series of the 21st Conference Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction PRES 2018. A total of 87 papers have been invited for full paper submission for this SI, and 34 highquality papers are accepted. The review is structured into three main sections. Section 2.0 discussed the advanced tools development for cleaner production, specifically on Pinch Analysis, Process Graph, Artificial Intelligence (AI) with Analytical Network Process (ANP) and Computer-aid modelling. Section 3.0 summarised the progress in renewable energy system technology. Section 4.0 is on waste as secondary resources process design. 2.0

Advanced Tools Development for Cleaner Production

2.1

Pinch Analysis Process Integration approach emerges as an effective tool for resource conservations in

chemical industries for decades (Klemeš and Kravanja, 2013). It originated offering targets in resource utilisations and been later extended to reduce wastes formations, emissions and pollutions to the environment. Pinch Analysis is well-known for its efficiency in solving the resource minimisation problem using thermodynamics concept. Linnhoff et al. (1982) pioneered this approach for energy saving within a chemical plant, even before specifying and knowing the mechanical ratings of the heat exchangers. Since then, the methodology inspired many applications in various domains, ranging from resource conservation to other nonconventional fields. Examples of successful applications are mass exchange networks (El

Journal Pre-proof Halwagi and Manousiouthakis, 1989), regional resources planning (Lam et al., 2010), power system planning (Jia et al., 2016), and even scheduling problems (Foo et al., 2007). One of its main contributions to emissions mitigation is the work by Tan and Foo (2017), which focuses on carbon emissions management to facilitate cleaner production. Comprehensive review and contribution of this methodology can be found in Klemeš et al. (2018). One of the papers in this SI utilised the ‘Pinch’ concept to design hydrogen separators with hydrogen network synthesis, considering the hydrogen solubility in oil product (Huang and Liu, 2019). The step-by-step method could optimise the separator parameters and target the minimum hydrogen consumption efficiently. Their graphical framework allows good visualisation, but only limited to a single separator. Another paper in this SI attempted to solve for segregated resources targeting problems with multiple objectives (Jain and Bandyopadhyay, 2018). Based on their work, a set of multi-objective problems is made up of various resource allocation and minimisation problems, with common internal sources and multiple sets of demands. They proposed a sequential algorithm to solve the problem. The Pinch Analysis is first used to solve individual problems. The utilised sources are then transferred to the next problem. Using the ‘Pinch’ Point of the last problem (i.e. the worst ‘Pinch’ quality compared to the others), they derived a quantity called multiple-objective prioritised cost to identify the optimal weighting factors for the objectives. The prioritising sequences for the multi-criteria targeting problems can then be determined from the factors. Their work serves as an innovative platform to analyse a multi-objective problem, but only limited to linear models. This issue is addressed in another article from this SI by Arya and Bandyopadhyay (2019). They proposed an iterative framework that utilises the Pinch Analysis concept to solve for non-linear resource targeting/minimising problem. They assume the quality of the internal sources are not deterministic and follows the normal distribution with known mean and standard deviation. The conservative linear problem using the mean values is first solved using Pinch Analysis, then the resultant network is solved for each iteration with a percentage of the previous solution. The proposed method is demonstrated in different case studies. Their framework could reduce computational burdens, but it does not guarantee global optimality. More complex non-linear models can be incorporated in future research to showcase the practicality of the model for real scenarios. Their research contributions aid in the advancement of the methodology towards its applicability in complex problems of resources/wastes minimisation for cleaner production.

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Process Graph (P-graph) Another well-known tool in the field of process network synthesis is the Process Graph

(P-graph), developed by Friedler et al. (1992b). It is open-source software that utilises the mathematical graph theory to solve for network-like optimisation problems (P-graph, 2015). The built-in algorithms: Maximal Structure Generation (MSG) developed by Friedler et al. (1993) and Solution Structure Generation (SSG) developed by Friedler et al. (1992a) generate the maximal structure (superstructure) of the problems and exclude redundant network structures, which aid the optimisation solver to perform efficient search in a reduced solution space. The combinatorial solution search algorithms such as decomposition and accelerating branch-and-bound (ABB) are provided in the software tool. One of the earlier contributions of P-graph in designing sustainable systems is made by Varbanov and Friedler (2008). They applied the methodology to investigate the combinative use of fuel cells with turbines to generate energy using biomass and/or fossil fuels. Another work by (Lam et al., 2010) is focused on the regional energy supply chain synthesis, which emphasises the use of biomass to supply energy. The work is later further developed by Lam et al. (2012) for open-structure biomass and biofuel production network, and How et al. (2016) who integrated the P-graph framework with mathematical modelling methods to synthesis multiple biomass corridors, considering different technology pathways and product formations. Cabezas et al. (2015) had also incorporated the ecological footprint calculation that emphasises the land uses into the methodology to design a sustainable and cost-effective energy supply system. Heckl et al. (2015) further extended the framework with emergy analysis as an evaluation tool for the electricity options. Éles et al. (2018) also utilised the P-graph methodology to optimise the energy option for an electronics manufacturing company using biomass and natural resources. It is worthy to note that Lim et al. (2017a) also applied the Pgraph methodology in synthesis of water allocation network, covering intra- and inter-plant integration, while further extended with water regeneration incorporation by Lim et al. (2017b). Chin et al. (2019) utilised the P-graph algorithms as advantages to solve for the non-linear formulation of a Heat-Integrated water network problem, covering isothermal and nonisothermal mixing scenarios. The further enhancements and applications of P-graph have been described by Klemeš and Varbanov (2015). Tan et al. (2018) had surveyed the unconventional applications of P-graph in sustainable systems with problem structure domains that have structures similar to process network synthesis.

Journal Pre-proof In this SI, one of the papers applied the P-graph methodology effectively in the application of negative net emissions through biochar management (Aviso et al., 2019a). The pyrolysis plants produce biomass with stabilised carbon in recalcitrant form, which could be stored under the soil for the long term. This brings the benefits for agricultural land as the biomass can be reused as fertilisers. However, some contaminants in the biomass limited its distribution into the soils. They proposed a framework, similar to resources allocation model, to maximise the benefits of biochar carbon and minimise the potential wastes. Since it is a resource allocation problem, it can be solved with Pinch Analysis as described before, which is done by Tan et al. (2018). However, the capability of the P-graph framework in generating sub-optimal solutions through SSG algorithm provides the added merits for its application. The user can explore the solutions through the network topology, identify the bottlenecks and conduct sensitivity analyses. This function is especially useful for the allocation problem in the biochar carbon management networks. The biochar allocation options can be explored with different flowrates with optimal overall biochar amount while fulfilling the contaminant constraints of the soils. As the optimal solutions occasionally are not desired due to its low practicality, the near-optimal solutions can be potential alternatives. This added benefit is also exploited by Lim et al. (2017a) in solving water allocation network and Chin et al. (2019) in solving heat-integrated water networks. Another paper in this SI also utilised the concept for non-conventional applications: human resources planning in universities (Aviso et al., 2019b). P-graph is used as a decision support tool to aid in planning the expansion of staffing levels for Higher Education Institutions. These institutions are transitioning from teaching-intensive to research-oriented, which is crucial in driving the eco-innovation of the developing countries. Their model provides good visualisation of the problem, which allows the management to plan for the staff transition easily. Knowledge expansion, dissemination and transfer are the keys in providing new solutions to environmental issues faced by industry (Pham et al., 2019), advancing towards the Sustainable Development Goals - SDG (SDG, 2019). 2.3

Artificial Intelligence (AI) with Analytical Network Process (ANP) Another widely used approach in decision making is the Analytic Network Process

(ANP) model. This approach is the extension of the Analytic Hierarchy Process (AHP) framework. AHP structures a decision problem into a hierarchy with a goal, decision criteria and alternatives (Aminuddun et al., 2014). Its roles in multi-objective ranking for decision making are written in Madu et al. (2002), which are summarised below:

Journal Pre-proof (a)

Identify all the tangible and intangible factors of the considering problem.

(b)

Major stakeholders participate in the decision-making process. It allows them to reach a consensus while ensuring consistency in their judgement.

(c)

It breaks down a complex problem into a decision hierarchy.

(d)

It facilitates the acceptance of the final outcomes by the key stakeholders.

Ayag and Ozdemir (2009) pointed out that the AHP model cannot accommodate the interactions, interdependencies and feedbacks between higher and lower-level elements. ANP is capable of modelling the complex correlations between elements as it represents the problem as a network. It is the alternative to the AHP and other multi-objective approaches due to its flexibility in identifying the significant inter-dependencies in ‘dynamic’ fashions, through the dynamic inputs from the policymakers (Sarkis, 2003). It is usually the preferred choice of a model when dealing with real problems with complex non-linear networks (Dou et al., 2014). To demonstrate a few examples, Nguyen et al. (2014) performed analysis on the variables that affect the machine tool selection effectiveness, with the use of fuzzy approaches in representing the uncertainties in the factors. Lin et al. (2015) applied the method in developing an assessment framework for green supplier section at a Taiwanese Electronics Company. More information on the methods descriptions and applications of the ANP model in various fields can be found in Lin et al. (2015), in terms of social, environmental and economic developments. Leong et al. (2019) contribute to this SI with the utilisation of the ANP approach, combined with a powerful optimiser for lean and green process operations. They proposed a framework that combines the back-propagation algorithms (Rumelhart et al. 1988) for further enhancement of the lean and green model, guiding the industrialist for continuous improvement. This algorithm has been promoted in one of the well-known artificial intelligence (AI) approach: the artificial neural network (ANN). They applied their framework in an industrial combined heat and power (CHP) plant to showcase the practicality. Experts opinions are first collected to determine the priority of the lean and green components using the ANP method. The optimiser is then used to optimise the factors within the network, which adaptively update the weights due to dynamic inputs. They also proposed a lean and green index to serve as the benchmarking tool for the plant practitioners, allowing systematic enhancement of plant performance in these aspects. Their adaptive approach allows the dynamic optimisation of lean and green index to be within 1.3 % of fluctuation within the targeted goal.

Journal Pre-proof Other than the ANP approach, artificial neural network (ANN) is also a widely applied tool in modelling the complex non-linear problems. It is a black-box model, but it is a useful tool in estimating the complicated relationship between input and output variables without the detailed knowledge of the system. The optimisation procedure in the ANN is used to identify the weights between independent and dependent variables, with the primary objective of minimising the errors between the model and the data (in the case of supervised learning). The back-propagation optimisation algorithm is popularised in ANN application by Rumelhart et al. (1988). However, the computational burden can become cumbersome when the network is complicated. This framework can be integrated with engineering process design to optimise the process functionality. Dhanarajan et al. (2017) stated that the combination of ANN with genetic algorithm (GA) is very efficient and effective in accurately simulate and model the non-linear problems in the sectors of engineering. More information about the application of ANN can be found in the state-of-the-art survey by Abiodun et al. (2018), while the combination of evolutionary algorithms with ANN can be found in Singh and Dwivedi (2018). One of the published works in this SI applied the ANN-GA methodology in optimising the NOx reduction in a submerged combustion vaporiser (SCV), which generally used at a liquefied natural gas (LNG) terminal (Shin et al., 2019). The LNG and supply of natural gas are vaporised to supply both industrial and domestic markets. In their work, they have the experimental set-up of the SCV, and effects of process variables on NOx formation in the flue gas are investigated. ANN algorithm is used as a surrogate model to simulate the complex relationships between the variables, and GA is used to optimise the NOx removal based on the ANN model. The results are validated through empirical verifications under the identified optimal conditions. They showed that the optimal for NOX removal is predicted to be 26.68 % which is validated through empirical verification that show 0.3 % prediction errors. Similar work has been done by Nayak et al. (2018) for an algal biorefinery. ANN-GA is employed to model the biomass of green microalga, using domestic wastewater as a culture medium and coal-fired flue gas as carbon sources. The tool is used to predict the optimal process conditions in this integrated process chain, based on the data collected from their experimental setup. Their work proves that the combination of ANN with evolutionary algorithms such as GA can be highly beneficial in clean production. The process can be modelled with ANN without detailed kinetic knowledge of the system while performing process optimisation with efficient algorithms. Their work is presently still at laboratory scale, but the researches strive forward the practicality of zero waste or emission productions.

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Computer-aided modelling In the era of advanced technology and computation power, computer-aided process

simulation is the core in the chemical process industry. It allows engineers a better understanding of the process systems, which enable the identification of bottlenecks and suggestion for improvements. Tula et al. (2018) stated that the sole reliance on process simulation tools as bases for process synthesis or operation is not enough to achieve the real optimum. The computer-aided tools for process engineering should be coupled with the field of product engineering so that the specific needs of the users or problems can be fulfilled. The authors presented a brief overview of the software tools available for this problem class. Among the software tools, the predictive model-based approach can provide innovative and sustainable solutions fulfilling the wide range of criteria. One of the potential applications is the simulation of CO2 flows in pipelines. CO2 has adverse global heating effects as a substantial part of greenhouse gas. The produced CO2 in the industrial processes should be preferably captured, transported and stored in underground sinks to protect the environment (Piipo et al., 2018). According to Jakobsen et al. (2017), the transportation of CO2 is most cost-efficient on land and transporting by pipelines is a preferable choice. Based on a report from International Energy Agency (IEA) in 2014 (IEA GHG, 2014), there are now a total of 7,000 km of CO2 pipelines globally and is estimated about 360,000 km of pipeline is needed to transport the industrially produced CO2 by the year of 2050. The report was based on 2014, and the number is currently growing due to the increased numbers of population and factories. The captured CO2 is also not entirely pure and may comprise of several contaminants, which might adversely affect the flow characteristics, and also might cause corrosion. The synergistic effect of electrochemical reactions, mass transfer and wall shear stress contributed by the impure CO2 flow on internal corrosion of pipelines should be considered (Hu and Cheng, 2016). These phenomena create problems in CO2 transportation and require deeper study and understanding of CO2 flow mechanics. Peletiri et al. (2019) contributed to this SI by simulating the CO2 fluids flowing in pipelines. Using different binary combinations of the impurities with CO2, along with specific parameters, they found out that almost all impurities have negative impacts on the CO2 flow performance. For example, Nitrogen (N2) with 10 mol% affects the greatest on pressure loss in a horizontal pipeline, also affecting the density and the critical pressure of CO2. Hydrogen sulphide with a maximum of 1.5 mol% has the lowest impact. The authors also studied the

Journal Pre-proof effects of different impurities and pipelines inclination angle on the pressure loss. Their results can serve as a guide while designing the CO2 pipelines as it shows the positive and negative impacts of each impurity. Similar work was also done by Hu and Cheng (2016), considering the oil-water two-phase flow in a pipeline that contains CO2. They conducted a detailed computational fluid dynamic (CFD) study on the effects of oil-water volume fractions on the pipeline wall shear stress. The substantial relating factors are the fluid flow velocity and the pipe inclination angle. Their empirical CFD model is capable of predicting the pipe corrosion rate and is validated with actual measurements. More information about the pipe corrosion mechanisms is available in Xiang et al. (2017), this authors provided state-of-the-art overview of pipeline steel corrosion mechanisms and models for impure CO2 during transportation. Other than the simulation of CO2 flow in pipelines, simulation of the process equipment incorporated with renewable resources is also receiving attention recently. One example is the study by Liu et al. (2015). These authors studied the post-combustion CO2 capture process for a supercritical coal-fired power plant (CFPP) through process simulation. The supercritical carbon emissions capture simulation model is also scaled-up to match the real capacity of the CFPP plant. To further improve the thermal efficiency, the authors combined the Heat Integration along with the simulation model. Their results show that the comprehensive Heat Integration, along with the proposed CO2 capture model, can improve the overall energy efficiency. One of the papers in this SI by Spitzer et al. (2019), conducted an investigation on the influences of membrane selectivity to study the performance of biogas plants through process simulation. Due to phased out European feed-in tariffs for biogas-based electricity, small stationary biogas upgrading plants need alternatives due to their high investment cost. The authors proposed a mobile membrane-based upgrading plants can help to reduce the investment, providing new valorisation routes. Based on their simulation study, increasing the ratio of CO2/CH4, the selectivity on the membrane increases the biomethane quality compared to the stationary upgrading plants using similar membranes. As the mobile upgrading plants are limited to two stages of membrane separation, it is crucial to select the proper membrane areas with adequate module characteristics. Their study shows the good result of CH4 recovery and energy efficiency, but the temperature of the fluid was not considered, and it could affect the membrane functionality significantly. In terms of product design, computer-aided analysis is also crucial in driving the chemical industries toward cleaner production. For example, Mah et al. (2019) applied the computer-aided molecular design framework in designing additives for fast pyrolysis bio-oil

Journal Pre-proof (from palm kernel shell). They aim to identify an optimal single solvent or solvent-oil blend to enhance the applicability of the bio-oil, in terms of its heating value, viscosity and phase stability. They incorporated thermodynamic analysis as well to study the phase stability between the solvent-oil blend. Neoh et al. (2019) conducted computer-aided solvent design for pyrolysis bio-oil as well, considering the blending functionality and sustainability aspects. However, their studies still require experimental validation to strengthen the results before it can be commercialised as a viable product. The combination of process simulation and product design can also be integrated to generate an environmental-friendly process and products. Papadopoulos et al. (2019) proposed a holistic framework to integrate sustainability assessment with computer-aided molecular design as well. Under the similar category of the sustainability assessment, an article from this SI combines the assessment with process simulation and product design to conduct a comprehensive analysis of chitosan microbeads (Meramo-Hurtado et al., 2019). They applied the approach in modifying titanium oxide (TiO2) nanoparticles for large-scale production of chitosan microbead. Chitosan is mainly used as a feedstock for bio-adsorbents production, modified with Ti O2 nanoparticles for pollutants removal. The degradation of the organic pollutants in air and water using TiO2as photocatalyst has been the central study for more than 30 y (Li et al., 2019). The process is first simulated with a commercial simulator to determine the necessary properties, and the environmental evaluation is then performed with another software. The assessment has been mainly focused on the environmental impacts and energy/exergy performance of the process. Their study allows the identification of strategies for process improvements to achieve better energy and environmental improvements. 3.0

Progress in Renewable Energy System Technology Several innovative process evolutions have been proposed to utilise renewable biomass

resources to produce virtually carbon-free products/fuels in this SI. For example, Chatrattanawet et al. (2019), presented the gasification of the sugarcane leftovers, coupled with the absorption process to produce syngas as well as a green liquid fuel. Their work focuses on the simulation of the process to identify the optimal condition through the use of various gasifier agents: steam, air and steam-air. They also propose to incorporate the absorption process to absorb CO2 and H2S using monoethanolamine (MEA) solvent to obtain clean production of the syngas. Another work included in this SI is to convert the empty fruit bunches (EFB) to hydrogen gas, which is stored as ammonia (Ajiwibowo et al., 2019). The authors

Journal Pre-proof proposed that the biomass can undergo supercritical water gasification (SCWG) to produce hydrogen gas (H2), which removes the requirement of energy-intensive pre-drying. The H2 is then undergone Haber-Bosch process to produce NH3 and to be stored. Based on their simulation study, the EFB conversion can achieve theoretically higher efficiency compared to traditional processes. They also suggest incorporating Heat Integration in the system to prevent excessive exergy destructions of the system. Liu et al. (2019) conducted a comprehensive review of the pre-treatment of the biowaste through pasteurisation approaches, as well as the potential of producing bio-methane. The compositions of the pathogens are also analysed based on previous works as well as the currently practised methods from different countries are studied. Another related work in this SI extends the biowaste utilisation to supply chain optimisation, considering the poultry litter as the biomass sources (Ma and You, 2019). Several processes such as thermal conversions, oil upgrading, hydrogen productions and power generations are considered. The optimal pathway is identified through the maximisation of return on investment (ROI). Vondra et al. (2019) also conducted a techno-economic assessment on a liquid digestate evaporation treatment in biogas plants for integrating evaporators in the plants. The creative equipment design to enhance the efficiency of renewable energy systems is also presented in this SI. Dedić-Jandrek and Nižetić (2019) proposed a small-scaled Archimedes screw turbine design for a hydropower station. They conducted an experimental investigation on the design issues of the turbine by manipulating several variables, such as water flow ratio, rotation speed and inclination angle. The Archimedes screw is fixed in an iron trough that could reduce the water hydraulic loss, which in turns improve the turbine efficiency. From the results, the inclination angle is the leading factor for the turbine efficiency and with the alternate-current (AC) generator gives a better turbine performance. Greater improvements and testing need to be done for the design to achieve wider applications. Qi et al. (2019) studied the cooling water system (CWS) optimisation that is used to discharge industrial waste heat, including any renewable energy technologies. They formulated a mixed-integer nonlinear programming (MINLP) model to design the system network and equipment design, featuring coolers, pumps, cooling towers and air coolers. They consider not only economic performance but also consider environmental performance such as water footprint and carbon footprint. Their model aims to facilitate cleaner production in a CWS through network and equipment design.

Journal Pre-proof Renewable energy has been a hot topic of recent research. Its significant advantage is lower GHG footprint compared with conventional fossil fuels power plants. However, it can be a potential problem due to the broad range of uncertainties existing in renewable energy systems. The uncertainties range from fluctuations in raw materials quality and availability, market demand to fluctuating power output intensity. Lim et al. (2019) proposed in this SI an element targeting approach in a multiperiod supply chain to improve biomass feed selection and utilisation. Their approach provides an optimal strategy for biomass storage using predicted resources and demand fluctuation. Each biomass processes in the processing hubs have different biomass element requirements. The element deficiency of each process can be identified and to predict the biomass importation to the hubs. It enables the prioritisation of several local biomass sources so that the biomass utilisation is maximised. Similar work also has been done by Kong et al. (2019) to consider the market demand and power output fluctuations. The renewable energy electricity supply chain collaboration model is constructed, integrated with the revenue-sharing contract. It is to ensure the optimal power grid-connection and consumption advancing towards greener energy supply. The continuous random variables are used to describe the intermittency of green power output intensity and power market demand fluctuations. The profit distribution between the power generator and the grid company by adjusting the revenue-sharing contract can be coordinated. The authors also analysed the optimal decisions taken by the companies for different power market demand price. The authors obtained the balanced management solutions of contract satisfied various conditions and investigated the relationship balancing the optimal variables and profits. It can be a fully guided framework for managerial decision making. Zakaria et al. (2019) reviewed stochastic optimisation methods in the field of renewable energy systems. The stochastic optimisation provides enhanced performances and is capable to more accurately represent the uncertainties of renewable systems. The authors also found that the model-driven approaches alone could not adequately address and handle the underlying complexity in vast multivariate and expanding renewable systems. The data-driven model with scenario analysis could be a valuable future choice (Chen et al., 2018). For renewable energy integration, when the problems are of higher dimension, there is a need to hybridise the existing optimisation methods with intelligent search. Those methods can reduce computational time with good accuracy (Dufo-López et al., 2016). Zakaria et al. (2019) also identified further research directions for stochastic renewable energy problems such as

Journal Pre-proof a) Plug-in electrical vehicles integration – for example. charging and scheduling of plug-in EV, renewable energy integration via vehicle to grid operation (Thompson, 2018) b) Demand-side management c) Spatial and temporal distributed renewable energy systems. Another paper in this SI proposed an innovative chemical way of energy storage while reducing the CO2 emission (Hadjadi et al., 2019). They proposed a theoretical mechanism of reducing CO2 and convert into methanol and methane through reverse water gas shift reaction (Samimi et al., 2019). Large varieties of solid catalyst have been developed and tested for this reaction, but the detailed reduction mechanisms are still unknown to many researchers, so it does not receive any attention (Sheldon et al., 2017). Hadjadi et al. (2019) conducted a thermodynamic analysis with up-to-date computational chemistry tools (Varandas, 2018) to identify all the possible structures of CO2 reaction pathways into methanol and methane. Their analysis shows the methane and methanol productions are at high thermodynamical efficiency and possible to be produced. The selection of right catalysts and degree of hydrogenation, the CO2 can be an electrical energy storage material alternative. The conversion of CO2 into methane and methanol provides added merits to energy saving. They can be supplied into the natural gas grid. The coordination of resources, flows and storages allocation with clear sustainability goals should be the core for the sustainable supply chain. Saavedra et al. (2018) presented a literature overview from 2007 to 2017, focusing on system dynamics modelling applied in the renewable energy supply chain. The authors showed that the system dynamics approach provides a harmonious relationship between the subsystems and processes with clear insights of system behaviours. The testing policies for improvement and their corresponding impacts can be assessed over time through system dynamic analysis. Azevedo et al. (2019) performed a comprehensive bibliometric analysis of studies in supply chain performance and renewable energies. The review was focused on the most productive authors and institutions, as well as the most cited articles from the field. According to Azevedo et al. (2019) most articles in the field focus on the design optimisation of renewable energies supply chain. Among the analysed methods, the Mixed-Integer Linear Programming (MILP) Model is the most popular.However many other methods and case studies, surveys, simulations, modelling, genetic algorithms, multi-scale modelling, and optimisation, are being used successfully.

Journal Pre-proof As mentioned in the previous paragraph, a massive number of models are published recently to tackle complex sustainability-related problems. However, the model is becoming more challenging to comprehend and understand, which limited the application. An algorithm should be developed to analyse the model complexity and provide information to the modellers. In another of the SI contributions presented by Honti et al. (2019), the authors studied an automated structural analysis that is capable of determining the importance of variables, identify structural modules and measure the complexity of the models. The model is first transformed into a network; the structural importance of variables or parameters are then evaluated. From the past 5 y, the developed system dynamics models can be used to capture the complex inter-relationship between the variables, in terms of the environment, economy and social aspects. The policymakers and engineers could analyse the sustainable network model that supports decision making in achieving the goals of cleaner production and more sustainable process. More information on the 130 system dynamic models can be found in this SI paper by Honti et al. (2019). 4.0

Waste as Secondary Resources Process Design The compositions and characteristics of waste vary for different cities, countries and

regions. An adequate design of waste management system is highly dependent on the waste amount, the composition of the waste (Ghinea et al., 2016), current waste separation practices in place, resources, infrastructure and facilities. Various approaches have been applied to identify suitable waste treatment options and management systems design. It can be generally divided into heuristic methods, multi-criteria decision analysis, graphs and network theory, mathematical optimisation, stochastic process techniques and statistical methods as summarised in the review by de Souza Melaré et al. (2017). Morrissey and Browne (2004) stated that the decision-making models that they applied in waste management could be divided into three categories (1) Cost-benefit analysis, (2) LCA and (3) Multi-objective approach. Review by Allesch and Brunner (2014) stated that more than 41 % of studies are performed by Life cycle assessment, 6 % by cost-benefit analysis and 10 % by Multi-Criteria Decision Making. A better classification has been presented by Cobo et al. (2018). Those authors classified the approaches in waste management studies into two major groups: system engineering models and system assessment tools. System engineering models focus on supporting the design of the system, while system assessment tools focus on evaluating the performance of the existing system. Sustainability analysis requires an integration of these two approaches. A detailed review of system analysis for solid waste treatment has been done by

Journal Pre-proof Chang et al. (2011), where the technology hub is presented (Figure 6). The five systems engineering models (in the circle, LP, DP, MIP and NLP are optimisation model) can be served as the core technologies where the model-based decision support system can be constructed for separate or collective applications. Chang et al. (2011) stated that the graphical decision support systems or expert systems can still be formed according to heuristic approaches using the rest of system assessment tools (the 8 triangles).

Figure 6: The technology hub for waste management system analysis. Adapted from Chang et al. (2011). A comprehensive review focuses on the sustainability of solid waste management has been discussed by Das et al. (2019). The challenges include the waste generation, waste collection, transport, as well as the treatment and disposal processes, are evaluated. Das et al. (2019) suggested that integration of artificial intelligence (Robotics), sensor-based waste type detection technique, RFID/GSM based waste logistic tracking systems etc. are some of the promising technological interventions that bear significant utility in the future waste management sector. There have been various waste treatment technologies including incineration, gasification, combustion, pyrolysis, anaerobic digestion, fermentation, carbonisation, mechanical extraction and composting. Those treatment approaches with material or utility recovery serve as an alternative of disposal to the landfill which is against the circular economy system. Ziegler-Rodriguez et al. (2019) performed a life cycle assessment to analyse the environmental benefits of diverting the waste in Peru (different geographical

Journal Pre-proof area i. hyper-arid cost; ii. Andean highlands; iii. Amazon rainforest) from open dumping to landfill with energy recovery. The reduction in GHG emissions is identified to be as high as 50 %–76 %. Waste to energy plants also generate waste, which includes fly ashes and bottom ashes, along the waste treatment process. Carbonation with different CO2 sources and stabilisation with cement solidification are among the treatment approaches to minimise the potential environmental impacts from the ashes. Margallo et al. (2019) compared the environmental impacts of these two approaches by life cycle assessment. Carbonation is generally having a better environmental performance specifically operated at flue gas pressures between 3-5 bar. The results of life cycle assessment are highly dependent on the system boundary. Although cradle to grave is the most promising analysis, it is sometimes being simplified due to the limited availability of data. Figure 7 shows the global warming potential (GWP) of different recycling approaches. This shows that recycling processes may create burdening footprints in the meantime of mitigating the footprint (e.g. GHG emissions) from the generated waste through recovery. Incineration and pyrolysis are the main GWP contributors, while recovered materials and heat are minimising the impact. However, the overall GWP is hardly offset. This highlights the importance of waste prevention in line with the established waste management hierarchy. It is essential to consider both burdening and unburdening effect (Kravanja and Čuček, 2013) on the environment in life cycle assessment.

Figure 7: Global Warming Potential (GWP) of different recycling approaches, adapted from Faraca et al. (2019)

Journal Pre-proof A mathematical model is among the most common waste management system analysis. The compromise between economic and environmental has been received high research attention due to the increasing concern and its challenging nature. Kůdela et al. (2019) present an approach utilising a multi-objective two-stage mixed-integer stochastic programming model for the design of transfer stations. The trade-off between the environmental and the economic is considered and demonstrated through a case study of the Czech Republic. The optimal decision suggests the establishment of 83 transfer stations with total capacity 4.25 Mt. Ongpeng et al. (2019) developed a model formulated as a Mixed Integer Linear Program to optimise the assignment of construction companies. The objective function is to minimise total carbon footprint during reconstruction while considering the size and classification of company size as well as project deadlines. The selection and planning of construction companies for a reconstruction project contributes substantially to cleaner production. The scenarios of having local and outside construction companies in the reconstruction project region resulted in an increase of 36.89 % carbon emissions footprint. In the study by Ang et al. (2019), a multiperiod and multi-criterion non-linear programming model is developed considering economic and environmental trade-off, alternative plant configurations, reuse and disposal option. The applicability is demonstrated through a wastewater treatment company in the Philippines. Šomplák et al. (2019) utilised both pricing and advertising principles in the mixed-integer linear programming model while accounting two criterions - assessment of greenhouse gas (GHG) and cost minimisation. The aim is to design the optimal waste management grid to suggest a sustainable economy with environmental concerns. Although mathematical model plays a significant role in supporting decision making, it is comparatively difficult to understand the reason for obtaining the optimal solutions, and in communicating the results with decision-makers, specific mathematical modelling knowledge is needed. As shown in Figure 7, the waste treatment process is generating environmental footprints. Improving process efficiency can significantly contribute to cleaner production. Konist et al. (2019) compare the emissions from typical oil shale combustion to the oil shale combustion with pyrolytic wastewater. Incineration of pyrolytic wastewater is an option (lower CO and PM from oil shale combustion), but wastewater injection increased fuel consumption (6 %). Vuppala et al. (2019) optimised the Olive Mill Wastewater treatment (coagulation and flocculation) by varying the pH and coagulant dosage values. The efficiency is evaluated through turbidity, Chemical Oxygen Demand (COD), Total Organic Carbon (TOC) and phenols. It was observed that 60 min of sedimentation was enough to achieve a 99 % reduction

Journal Pre-proof of turbidity for both alum and chitosan. Fekhar et al. (2019) compared the product properties and stability of light oils obtained by thermal and thermo-catalytic pyrolysis of real waste plastic high-density polyethylene, low-density polyethylene, polypropylene, and polyvinyl chloride) in a horizontal tubular reactor. The catalysts capable of increasing the yields of recovered gases (from 8.6 % to 21.7–14.1 %) and light oil (from 38.9 % to 59.7–49.1 %). To further improve the cleaner production, the system design should be considered their nexus or integration rather than individually. This can prevent the shift of environmental footprints. Garcia et al. (2019) developed a Food-Energy-Water-Waste Nexus (FEWWN) to study how food, water, energy and associated waste interact. It is capable of design FEWWN systems to produce a set of product or products that maximises system financial profits while maximising the region's Green Gross Domestic Product (GDP), or the sum of nominal GDP and the value of all ecosystem services in the region. 5.0

Conclusion This special issue introduced and assessed several recent research of optimisation tools

and process design which can significantly promote the development of cleaner production. The selection, based mainly on the works presented at the PRES 2018 conference, covers a wide and vital part of this field. The relation of cleaner production and sustainable development was studied by several papers included in the PRES 2018 Special issue. The results indicated that the steel and cement industries account for more than 40 % of CO2 emitted by the industry and estimated more than 50 % of global anthropogenic CH4 would be emitted in 2020 by the source from enteric fermentation and oil and gas. Another significant observation are values of climate, material, health, land and ecosystem footprints. It has also been observed that waste disposal and treatment after the life of renewable sources of energy are issues to be seriously considered. Advanced optimisation and design tools development for cleaner production have been analysed, including Pinch Analysis, P-Graphs, Artificial Intelligence (AI) with Analytical Network Process (ANP) and Computer-Aided modelling. Each of those tools demonstrated by selected SI papers considerable potential for cleaner production, e.g. one of the proposed adaptive AI approaches could contribute to the achievement of the targeted sustainable goal with dynamic change and keep it within a 1.3 % fluctuation. In another study, the proposed AI approach in modelling NOx reduction of a submerged combustion vaporiser shows a high accuracy where the prediction error is lower than 0.3 %.

Journal Pre-proof Progress in Renewable Energy System Technology and Waste as Secondary Resources Process Design were reviewed in the concluding part. Some interesting potential for improved waste treatment in a developing economy country - Peru - was identified as high as the reduction in GHG emissions in the range of 50 %–76 %. It has been evident that those are spots where very considerable savings can be achieved. The authors of this editorial believe that selected studies supplemented with a variety of examples would contribute to the further development of cleaner production achievements. Acknowledgement: The EU supported project Sustainable Process Integration Laboratory – SPIL funded as project No. CZ.02.1.01/0.0/0.0/15 003/0000456, by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research based on the SPIL project have been gratefully acknowledged in collaboration with Research Institute of Sinopec, Shanghai, China. References Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., Arshad, H., 2018. State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. Aguilera, E., Vila-Traver, J., Deemer, B. R., Infante-Amate, J., Guzmán, G. I., Gonzalez de Molina, M., 2019. Methane Emissions from Artificial Waterbodies Dominate the Carbon Footprint of Irrigation: A Study of Transitions in the Food–Energy–Water–Climate Nexus (Spain, 1900–2014). Environmental Science & Technology, 53(9), 5091-5101. Ajiwibowo, M. W., Darmawan, A., Aziz, M., 2019. Towards clean palm oil processing: Integrated ammonia production from empty fruit bunch and palm oil effluent. Journal of Cleaner Production, 236, 117680. Allesch, A., Brunner, P. H., 2014. Assessment methods for solid waste management: A literature review. Waste Management & Research, 32(6), 461-473. Al-Naiema, I., Estillore, A.D., Mudunkotuwa, I.A., Grassian, V.H., Stone, E.A., 2015. Impacts of co-firing biomass on emissions of particulate matter to the atmosphere. Fuel, 162, 111–120.

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