Lancaster University Showcase

Spreadsheets for the 7 scenarios investigated in the article: "Aspen Plus® process simulation model of the biomass ash-based treatment of anaerobic digestate for production of fertilizer and upgradation of biogas". A comparison of the following 7 process strategies is mentioned in the above article: Case 1 is the foundation case (labelled as untreated MD) that served as benchmark and it implied the production of MD using the original PSM of Rajendran et al. [11]. In Case 2 a stream of pure hydrochloric acid was incorporated at a 0.1000 times the flowrate of the MD towards the ionization reactor (1HCl:10MD). In Case 3 a stream of hydrochloric acid was incorpo-rated at 0.1176 times the flowrate of the MD towards the stoichiometric-equilibria reactor (1HCl:8.5MD). In Case 4 a stream of hydrochloric acid was incorporated at 0.1212 times the flowrate of the MD towards the ionization reactor (1HCl:8.25MD). The remaining 3 cases are built on Case 3 (i.e. considering the previous acidification of the MD with the dose of 3.18 mEq HCl/g). In Case 5 the stream of SSA (Table 1) was incorpo-rated at 0.0040 times the flowrate of the MD towards the stoichiometric-equilibria reactor (1SSA:1.76HCl:15MD). In Case 6 the stream of ash (Table 1) was incorporated at 0.0060 times the flowrate of the MD towards the ionization reactor (1SSA:1.18HCl:10MD). In Case 7 the stream of ash was incorporated at 0.0080 times the flowrate of the DM to the stoi-chiometric-equilibria reactor (1SSA:0.88HCl:7.5MD). Additionally, MS Word file summarizes the most relevant data: Table S1, Mass balance of the nutrients monitored in the Aspen Plus® simulations. (1/3); Table S2, Mass balance of the nutrients monitored in the Aspen Plus® simulations. (2/3); Table S3, Mass balance of the nutrients monitored in the Aspen Plus® simulations. (3/3); Table S4, List of the components in the PSM of Rajendran et al. [11]. (1/3); Table S5, List of the components in the PSM of Rajendran et al. [11]. (2/3); Table S6, List of the components in the PSM of Rajendran et al. [11]. (3/3).

Noble gas and stable isotope data from Anyue gas field, China

Soil columns permeated by roots with and without root hairs to assess the contribution of root hairs to soil reinforcement under shear stress.

All data for "Osmolality as a novel mechanism explaining diet effects on the outcome of infection with a blood parasite" paper in Current Biology 2020

Complete results corresponding to the research presented in the paper entitled 'A compact reformulation of the two-stage robust resource-constrained project scheduling problem', submitted to 'Computers and Operations Research' in April 2020 (Bold and Goerigk, 2020). The uncertain resource-constrained project scheduling problem (RCPSP) test instances on which these results are obtained are derived from deterministic instances in the PSPLIB (http://www.om-db.wi.tum.de/psplib/) involving 30 activities (j30). 3 robust counterparts corresponding to the delaying of (Gamma=) 3, 5 and 7 activities respectively have been generated for each of the 480 deterministic PSPLIB instances. Hence, a total of 1440 test instances have been generated. This data set contains four data files corresponding to the results of four variants of the model proposed in Bold and Goerigk (2020). These are a 'basic' model (basic_results_full.txt), and three extended models: including transitivity constraints (trans_results_full.txt), warm-start (warmstart_results_full.txt), transitivity constraints plus warm-start (warmstarttrans_results_full.txt). See Bold and Goerigk (2020) for details of these methods. Each data set reports for each instance the instance number (corresponding to the deterministic PSPLIB instance), the number of activities delayed (Gamma), the Gurobi solution status code, the best lower and upper-bound found by the solver, the gap between these two values, and the CPU run-time for that instance. Results show that the proposed model out-performs the current state-of-the-art algorithms for solving the two-stage robust resource-constrained project scheduling problem, being much quicker to solve, and reaching optimality for 50% more instances on the same benchmark set.

Datasets and Supplementary Materials

Processed PM2.5 (from U.S. diplomatic missions in India) and meteorology (from https://www.ncdc.noaa.gov/) at Delhi, Mumbai, Hyderabad and Chennai during 2015-2018. Dataset is in MATLAB format.

Table 3 – Further characterisation of the digestates (determinations made by NRM lab)

Table 5 – Procedures for the determination of pH and EC in digestate and soil samples

Table 4 – Further characterisation of the ashes (in house analyses)