A B S T R A C T The concept of sustainable risk assessment in industrial wastewater treatment is vital to determine the causes and consequences of plant failure. The potential wastewater-related risks that could hamper the operation of the entire manufacturing facility are currently inadequately defined and under researched. This work proposes a framework that includes the comparison of literature and experimental data to quantify the impact of the significant process parameters on the critical

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... process outputs. From the business perspective, managing and minimising risks will be possible when the number of impact parameters is low and the relationships between different parameters are clearly understood. The results show that even only the evaluation of technical risks can provide an assessment platform template for other risk types. Also, the structured and statistically analyzed data sets applied might be further used in the design and development of machine learning platforms algorithms to inform sustainable process outcomes adjusted for various geographical locations and human factors which significantly affect the industrial water sector globally. The risk of an emerging "global water crisis" is regarded as the third highest ranked risk, in terms of overall impact on the industry [2]. There is a clear need for the implementation of better practices and more holistic measures that can rectify risks related to water use on industrial sites. In common with many other industries, the water sector is formalizing explicit approaches to risk management and decision-making, that have formerly been implicit [8] . Many industries follow the accepted standards of performance and codes of practice [9] . However, the overall complexity of the industrial water sector may require the integrated risk analysis to better understand what drives the risk from or to the plant, process, or operation. Risk management frameworks establish a platform based on the risk identification, evaluation, and management to drive decision making by industrial stakeholders [10] . The framework of an iterative approach recognizes that continuous improvement of the risk analysis to develop a mature capacity in risk management [11] . The implementation of a portfolio of risk techniques within a water sector depends on enterprise size, existing management culture, and the governmental regulations [12] . The implementation of a risk assessment framework should not be considered as the end -point of risk management, but as an inspiration for the continuous process re-assessment to mitigate any potential risk [13] . Overall, risk analysis can be considered as a tool that informs the decision-making process [14] . The risk management activity should reflect sensible and meaningful conclusions rather than theoretical perspectives, as previously suggested [15, 16] . The efficient risk management of a water utility requires both methods for proper risk assessment and multiple criteria decision analysis [17] . Risks are not limited to the boundaries of a single enterprise and are often driven by customer pressures and increased stakeholder interest [18] . Thus, the dependencies between suppliers, customers, competitors, and organization must be identified, assessed, mitigated, and monitored. To the author knowledge, no previous work has been carried out on the development of qualitative methods for integrated risk assessment in the wastewater sector. Moreover, the previous business risk models were mostly used for the evaluation of risks arising from domestic water use [19, 20] . The emergence of industrial 4.0 technologies have enabled the detailed evaluation of various risks across business operations and supply chains [21] . The identification of risks arising within the framework of Industry 4.0 includes the data analysis and development of additional statistical tools [22] . The structural complexity of supply chain networks significantly influences overall system complexity and can be comprehensively assessed by statistical tools, i.e., JMP, MINITAB tools to reduce the amount of information needed to specify the system and its components [23] . Heat maps are often used with the simultaneous combination of univariate and multivariate data and statistical assessments for the statistical modeling of parameters [24] . Previous studies have recommended a risk modeling approach-framed by Industry 4.0 concepts that includes a clustered heat map to reduce the model complexity and includes options for the risk ranking [25] . Understanding risks originating from human errors, management weaknesses, technological failures, irrational exploitation of natural resources and generation of non-disposable waste can prevent industry from accidents, disruptions, and financial bankruptcy [26] . Risk management is often linked to significant uncertainties and associated complexity, which emphasize the need for the development of rigorous and sophisticated modeling approaches [27] . Supply chain consideration have attracted an increasing focus on wastewater-related risks [28, 29] . The aim of this paper is to provide a tool for qualitative and quantitative risk assessment, and decision support for industrial sites. One single approach cannot be used to access all risk-related problems [30] . Hence, quantitative methods such as risk ranking are also required. This work presents a new approach in assessing risks in a common structured way, using process performance metrics to develop a real time risk ranking methodology to guide risk-reduction measures and decision making. A decision model is developed that combines risk ranking with multi-criteria decision analysis and take uncertainties into consideration using statistical evaluation platform. Fig. 1 . Flowchart of the risk assessment methodology. A. Trubetskaya et al.