The aim of this study was to identify the most sustainable way to produce char from sewage sludge that later can be used as a coal substitute for the steel industry. For this purpose, a literature review was conducted on the latest technological developments for the production of char, which were subsequently simulated in Aspen Plus to obtain process-specific details. The results of the process simulation are then combined with the Life-Cycle-Assessment (LCA) database EcoInvent to assess the environmental impact of the entire value chain for all countries of the European Union using the LCA methodology according to the ISO 14040 framework and the environmental footprint impact family. This family consists of 19 impact categories of which five relevant categories were chosen: (I) climate change, (II) acidification, (III) freshwater ecotoxicity, (IV) freshwater eutrophication and (V) marine eutrophication. In a second analysis the LCA is combined with the integrated assessment model REMIND, which represents the technological evolution in the future. This allows to evaluate the environmental impact in the years 2030 and 2040. From the literature review, two technology developments that significantly can reduce the overall electricity consumption of the process were identified: an industrial dryer that can dry sewage sludge at 90% efficiency and a heat pump that can convert moist exhaust air into process heat with an efficiency of 400%. Furthermore, the Torwash process was identified, as a pre-processing step for sewage sludge, which increases the efficiency of the filter process and the subsequent drying of the sludge. Based on the results of the literature research, three different technology scenarios were defined. (I) In the baseline scenario, the gases and bio-oils formed during the slow pyrolysis reaction are burned to produce sufficient process heat for the pyrolysis unit and the drying step. (II) In the Torwash scenario, the feedstock is thermally treated before the filtration, which allows a higher solid content in the filter cake and thus reduces the energy consumption in the drying step. (III) The bio-oil scenario considers the condensation of bio-oils and provides the missing process heat with electric heaters. The process simulation in Aspen Plus was used to determine several process-specific details, including the energy content of the combustion process and the resulting CO2 emissions. Moreover, the overall energy balance of the different scenario was calculated and the need for external heat was determined. The process simulation additionally revealed a factor not described in the literature yet that influences the energy consumption of the pyrolysis reaction. The higher the ash content of the feedstock, the less energy is required for the pyrolysis process. In the LCA, the simulation results were combined with further background processes and environmental information from the LCA database EcoInvent and compared against a business-as-usual alternative. In this alternative, the sludge is incinerated and coal with a comparable heating value to the produced char is provided. The results show that the baseline scenario performs better than the business-as-usual alternative in all impact categories. The Torwash scenario, due to the high energy requirements of the pretreatment, performs worse than the baseline scenario in all impact categories and in all but freshwater eutrophication also worse than the business-as-usual alternative. For the bio-oil scenario, the type of heat supply and the CO2 intensity of the electricity grid is of great importance. If an inefficient resistant heater is used, the bio-oil scenario performs better than the baseline scenario in only half of the European countries, depending on the specific CO2 intensity of the national electricity mixes. If, instead, a highly efficient heat pump is used, the environmental impact is lower for all EU member states for all impact categories. The difference between the baseline and the bio-oil scenario becomes even larger when the environmental impact is determined for 2030 or 2040. If a conservative integrated assessment model which leads to 2.5°C warming by 2050 is used as the basis for technological change, the emissions of the bio-oil scenario are 3.020 kg CO2eq per ton of char, more than 9% lower than the baseline scenario and almost 42% lower than the business-as-usual alternative. In addition, to the ton of char, 120 l of bio-oil are produced, with an economic value that offsets the additional cost of electricity. Therefore, the strong recommendation is expressed to produce char of sewage sludge according to the bio-oil scenario, with includes the latest drying and heat-pump technologies as well as the co-production of bio-oil.

Purpose: This paper explores the potential of machine learning (ML) algorithms to mitigate uncertainty in early environmental assessments (ex-ante LCA), which are hindered by prospective nature and limited quantitative data availability. Methods: A systematic literature review with keyword searches on Scopus identified three ML categorization groups in ex-ante LCA: streamlined LCA, ex-ante LCA parameter projection, and ancillary models and data. Two following case studies addressed literature gaps in price forecasting for economic allocation and recycling rate projections. Results: In streamlined LCA, 16 studies linked molecular and technical parameters to project production-related emissions of organic chemicals, applied product clustering of product groups, and generated spatially explicit impact category results. The application of ex-ante LCA parameter projection, as evidenced by 14 publications, involves the use of ML to project life cycle inventory (LCI) data, project characterization factors, and integrate natural parameters with LCI data in a comprehensive modeling approach. In nine other papers the applications to ex-ante LCA remained undefined but potentially applicable. For both case studies, best results were obtained with a Recurrent Neural Networks (RNN) algorithm with long-short-term-memory (LSTM). Commodity price forecasting in the first case study achieved a projection accuracy of 0.96 (MSE), 0.98 (RSME), and 10.17% (MAPE) for copper and 88.86 (MSE), 9.43 (RMSE), and 21.23% (MAPE) for molybdenum. Probability modelling is identified as a modeling approach which incorporates uncertainty. The recycling rate forecast case study identified plastic recycling and glass recycling rates as the best suiting covariates and demonstrated multivariate modeling possibilities with 0.22 (MSE), 0.48 (RSME), and 0.38% (MAPE) in a model with 68 covariates. Discussion: A limited yet growing body of literature indicates that ML applications in ex-ante LCA represent an emerging field of science. While streamlined LCA shows promise, it faces constraints related to data precision and a static nature. In the ex-ante LCA parameter projection categorization, the sub-group of similarity clustering of LCI processes suffers from data uncertainty in LCI databases, making the approach more suitable for updates of existing technologies than for emerging ones. On the other hand, LCI generation through ex-ternal parameters represents a highly technology-specific case, showing significant promise. The projection of characterization factors and the sub-group of integrated modeling are identified as promising, but the limited number of scientific studies hinders the generalizability of these findings. Case studies on price forecasting and recycling rate projection demonstrate ML’s applicability in economic allocation and waste treatment projections. Overall, the results suggest that ML holds potential for reducing uncertainty in ex-ante LCA, laying the groundwork for focused research and contributing to a nuanced understanding of uncertainty reduction in this domain. Recommendations: The paper emphasizes the need for targeted research in the goal and scope phase and in End-of-Life (EoL) treatment forecasts, e.g. via the use of time-series multivariate modeling. Furthermore, it encourages further exploration of streamlined LCA into applications with a high degree of technical predictors, along with the extended projection of characterization factors and integrated modeling. Additionally, the use of probabilistic modeling as a tool to incorporate uncertainty into the modeling is recommended, aiming to enhance the applicability and transparency of ML applications for reducing uncertainties in ex-ante LCA.